Patent Publication Number: US-11645706-B1

Title: Generating detection parameters for a rental property monitoring solution using computer vision and audio analytics from a rental agreement

Description:
This application relates to U.S. Provisional Application No. 62/971,706, filed Feb. 7, 2020. This application also relates to U.S. patent application Ser. No. 16/858,230, filed Apr. 24, 2020, which relates to U.S. Provisional Application No. 62/949,049, filed Dec. 17, 2019. Each of the above mentioned applications are hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The invention relates to security cameras generally and, more particularly, to a method and/or apparatus for generating detection parameters for a rental property monitoring solution using computer vision and audio analytics from a rental agreement. 
     BACKGROUND 
     With the popularity of services like AirBNB, renting property is becoming increasingly popular and has less oversight. Rentals can be set up without the property owner and the renter(s) ever meeting in person. Property owners face the problem of how to protect themselves against renters holding unauthorized parties at rental properties. In addition to online rental services, property owners in areas that have colleges or universities have long had problems with renters involving large parties, loud noises, drugs and alcohol (i.e., keg parties). Additionally, some property owners want to protect themselves against renters bringing pets to the property. Large parties and renters not using a rental property as intended can result in damage to the property, liability issues for the property owner and/or late night angry calls from neighbors. Property owners cannot always be physically present to watch the rental property. 
     The use of existing video cameras to monitor properties to check on the compliance of renters is problematic because of privacy issues. Renters do not want video of themselves being viewed remotely, voice conversations being listened to, or any recordings, video or audio, being made. 
     Not all property owners have the time or technical knowledge necessary to configure the sensing camera to make sure that the renters are abiding by the terms of the rental agreement (i.e., number of people, pets, music/noise level etc.). Configuration settings for computer vision and audio analysis can provide an overwhelming number of options. Property owners and renters do not have a way to easily create a human-legible rental agreement that can be applied to a sensing camera to ensure that the rental agreement is properly being followed. 
     It would be desirable to implement generating detection parameters for a rental property monitoring solution using computer vision and audio analytics from a rental agreement. 
     SUMMARY 
     The invention concerns a system comprising a server computer and a camera system. The server computer may comprise a memory configured to store computer readable instructions and a processor configured to execute the computer readable instructions. The computer readable instructions may be configured to receive a rental offer for a location from a first user, receive a rental request for the location from a second user, generate a rental agreement comprising conditions in response to the rental offer and the rental request and generate a feature set based on the conditions, if the first user and the second user agree to the rental agreement. The camera system may be configured to receive the feature set from the server computer, convert the feature set to detection parameters for the camera system, generate a plurality of video frames of the location, perform video operations to detect objects in the video frames, extract data about the objects based on characteristics of the objects determined using the video operations, compare the data to the detection parameters and generate a notification in response to the data matching the detection parameters. The camera system may be implemented at the location. The video frames may be discarded after performing the video operations. The video operations may be performed locally by the camera system. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       Embodiments of the invention will be apparent from the following detailed description and the appended claims and drawings. 
         FIG.  1    is a diagram illustrating an example embodiment of the present invention. 
         FIG.  2    is a diagram illustrating an example embodiment of the camera system at the rental location. 
         FIG.  3    is a diagram illustrating monitoring rental properties using computer vision and/or audio analytics. 
         FIG.  4    is a block diagram illustrating an example embodiment of the camera system. 
         FIG.  5    is a diagram illustrating detecting a breach of a rental agreement. 
         FIG.  6    is a diagram illustrating an example notification for a breach of a rental agreement. 
         FIG.  7    is a diagram illustrating an example of comparing statistics and parameters extracted from video and/or audio data to entries of a rental agreement. 
         FIG.  8    is a diagram illustrating an example interface for a rental offer. 
         FIG.  9    is a diagram illustrating an example interface for a rental request. 
         FIG.  10    is a diagram illustrating an example interface for a rental agreement. 
         FIG.  11    is a flowchart illustrating a method for generating a rental agreement from user input. 
         FIG.  12    is a flowchart illustrating a method for loading a feature set to the camera system when the rental agreement is active. 
         FIG.  13    is a flowchart illustrating a method for performing the computer vision analysis of the video frames to detect a breach of the rental agreement. 
         FIG.  14    is a flowchart illustrating a method for generating a web-interface based on available features of the camera system. 
         FIG.  15    is a flowchart illustrating a method for communicating pre-defined sets of neural networks to the camera systems based on the position of the camera systems and/or the terms of the rental agreement. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Embodiments of the present invention include generating detection parameters for a rental property monitoring solution using computer vision and audio analytics from a rental agreement that may (i) perform video analytics locally, (ii) perform audio analytics locally, (iii) discard data after processing, (iv) perform video analytics without storing video data, (v) detect restricted activities, (vi) count a number of people at a location, (vii) detect an audio level at a location, (viii) provide a notification when a restricted activity is detected, (ix) provide a web interface for users to enter a rental agreement, (x) provide video and/or audio detection parameters to a camera system based on the rental agreement, (xi) preserve privacy of people at a rental property and/or (xii) be implemented as one or more integrated circuits. 
     Embodiments of the present invention may be configured to monitor a location (e.g., a property) and notify a property owner in the event of a restricted activity being detected. In an example, the restricted activity may be a party being held and/or the presence of a pet. Using camera technology and/or computer vision, data (e.g., parameters and statistics) may be extracted from captured images and/or sound. The data may be used to make decisions. The decisions may be determined based on the video data and/or the audio data. However, the video data and/or the audio data may not need to be stored and/or communicated. For example, after the decision has been determined, the video data and/or the audio data may be discarded. Discarding the video data and/or the audio data may preserve privacy. 
     The detection of restricted activities may be determined based on various search parameters performed using computer vision and/or audio analytics. In one example, whether a party is detected may be determined based on using computer vision to detect people and counting the number of people present at the location. In another example, analytics may be implemented to detect a sound level at the location (e.g., detect whether loud music is being played). The computer vision operations may be configured to detect and/or classify objects. In an example, the computer vision operations may be configured to detect animals. 
     Embodiments of the present invention may be configured to provide a notification to the property owner. The notification may comprise a warning notice. The warning notice may indicate that a restricted activity has been detected. In an example, the notification may be presented from a cloud service to a user device such as a smart phone. The cloud service may be configured to receive the decisions made in response to analysis of video data and/or audio data, but may not receive the video data and/or the audio data. 
     Embodiments of the present invention may enable use of a web-based application to facilitate generating an agreement to rent a property. The property owner may enter details about the rules for renting the property (e.g., number of people that will be present, whether loud music/movies are allowed, whether there will be a pet allowed, etc.). Based on the information submitted by the property owner, a web-based application may list the property as available for rent. Similarly, a prospective renter may enter details about a desired rental property into the web-based application. 
     The web-based application may automatically check the requirements of the renter against the rules defined by the property owner. For example, the owner may specify the maximum number of people allowed, whether a pet is allowed, and whether loud music is allowed to be played. If the requirements of the renter fall within the rules of the owner, then a rental booking may be made. The sensing camera may be automatically configured to monitor the property on the specific days rented according to the rules of the rental agreement. For example, the sensing camera may check for the number of people, pets, music etc. as defined by the on-line rental application contract completed by the renter and the property owner. The rental agreement may be used as a basis for setting various search and/or detection parameters for the computer vision and/or audio analytics performed by the sensing camera system. 
     Referring to  FIG.  1   , a diagram illustrating an example embodiment of the present invention is shown. A system  100  is shown. The system  100  may comprise one or more server computers  102   a - 102   n  and/or one or more camera systems  104   a - 104   n.  A number of user devices  52   a - 52   n  may be connected to the system  100 . The camera systems  104   a - 104   n  are shown at a location  50 . In an example, the location  50  may be a rental property. The server computers  102   a - 102   n  may comprise a number of computing devices configured to operate as a scalable cloud service. For example, the server computers  102   a - 102   n  may provide processing and/or storage that may be configured to scale based on demand. The server computers may be referred to generally as the server  102 , but maybe implemented using a number of computing devices  102   a - 102   n  configured to operate together. The system  100  may comprise other components and/or interconnections between the components. The arrangement of the system  100  may be varied according to the design criteria of a particular implementation. 
     Generally, the server computer(s)  102   a - 102   n  and the camera systems  104   a - 104  may be implemented at different locations. For example, the server computers  102   a - 102   n  may be implemented at a centralized location, and the camera systems  104   a - 104   n  may be implemented at the rental property  50 . While multiple camera systems  104   a - 104   n  are shown at the location  50 , in some embodiments one of the camera systems  104   a - 104   n  may be implemented at the location  50 . While multiple camera systems  104   a - 104   n  are shown at the location  50 , in some embodiments the server computers  102   a - 102   n  maybe configured to communicate with multiple camera systems  104   a - 104   n  that may be located at the same and/or different properties. For example, the system  100  may comprise the server computers  102   a - 102   n  and a plurality of camera systems  104   a - 104   n  that may be implemented at one or multiple locations. 
     In the example shown, the user devices  52   a - 52   n  may be desktop computers. However, the user devices  52   a - 52   n  maybe implemented as any type of computing device configured to connect to the internet, receive user input and/or provide a graphical display (e.g., a desktop computer, a laptop computer, a netbook, a tablet computing device, a smartphone, a phablet, a wearable headset, a patient monitor, etc.). Generally, the remote devices  52   a - 52   n  may enable communication to/from the servers  102   a - 102   n  and/or the camera systems  104   a - 104   n.    
     Each of the server computers  102   a - 102   n  may comprise a respective processor  110   a - 110   n  and/or a respective memory  112   a - 112   n.  In the example shown, the processor  110   a  and the memory  112   a  of the server computer  102   a  are shown as a representative example. The processors  110   a - 110   n  may be configured to execute computer readable instructions. The memory  112   a - 112   n  may be configured to store the computer readable instructions. 
     The processors  110   a - 110   n  may be configured to receive input from the user devices  52   a - 52   n,  operate on the input according to the computer readable instructions and generate output for the user devices  52   a - 52   n.  The processors  110   a - 110   n  may be configured to receive input from the camera systems  104   a - 104   n,  operate on the input according to the computer readable instructions and generate output for the camera systems  104   a - 104   n.  The processors  110   a - 110   n  may operate in parallel with each other to collectively perform computational tasks. 
     The memory  112   a - 112   n  may comprise a block  120 , a block  122 , a block  124  and/or a block  126 . The block  120  may comprise storage of rental listings. The block  122  may comprise storage of rental agreements. The block  124  may comprise a detection engine. The block  126  may comprise data for a web interface. The memory  112   a - 112   n  may comprise other components (not shown). The memory  112   a - 112   n  may comprise hard disk drives, cache memory, RAM and/or flash-based memory. The type of storage, the type of information stored and/or the arrangement of the data stored by the memory  112   a - 112   n  may be varied according to the design criteria of a particular implementation. 
     The server computers  102   a - 102   n  maybe configured to provide the web interface  126  to the computing devices  52   a - 52   b.  The web interface  126  may be generated by the processors  110   a - 110   n  in response to the computer readable instructions. For example, the computer readable instructions may comprise web design languages such as HTML, PHP, Javascript, CSS, etc. The data used to generate the web interface  126  may be generated from the listings  120  and/or the detection engine  124 . The web interface  126  may be configured to receive input from users. For example, the web interface  126  may enable users to list properties for rent, request a rental property, select terms and/or conditions for a rental agreement, agree to a rental agreement, modify terms of a rental agreement, etc. The listings and rental agreements generated from the web interface  126  may be listings  120  and the rental agreements  122  stored in the memory  112   a - 112   n.    
     In the example shown, one of the user devices  52   a - 52   b  (e.g., the user device  52   a ) may be used by a rental property owner and the other of the user devices  52   a - 52   b  (e.g., the user device  52   b ) may be used by a prospective renter. The property owner user device  52   a  is shown receiving a signal (e.g., WEB), generating a signal (e.g., RENTOFR), receiving a signal (e.g., RAGREE) and generating a signal (e.g., CONFO). The signal RENTOFR and the signal CONFO may be communicated to the server computers  102   a - 102   n  by the property owner user device  52   a.  The renter user device  52   b  is shown receiving the signal WEB, generating a signal (e.g., RENTREQ), receiving the signal RAGREE and generating a signal (e.g., CONFR). The signal RENTREQ and the signal CONFR may be communicated to the server computers  102   a - 102   n  by the renter user device  52   b.  The signal WEB and the signal RAGREE may be communicated by the server computers  102   a - 102   n  to the user devices  52   a - 52   b.    
     The signal WEB may represent the communication of the web interface  126  to the user devices  52   a - 52   b.  The rental property owner may use the web interface  126  to provide a rental offer. For example, the signal WEB may be configured to provide a listing form interface for the web application  126 . The rental offer may be communicated to the server computers  102   a - 102   n  as the signal RENTOFR. The rental offer may list terms and/or conditions that the property owner has for the rental property  50 . The server computers  102   a - 102   n  may store the rental offers as the listings  120 . For example, the listings  120  may be generated based on the rental offers provided by various property owners. Details of the rental listing web interface  126  may be described in association with  FIG.  8   . 
     The signal WEB may be provided to the renter user device  52   b  to enable browsing the listings  120 . The property renter may use the web interface  126  to request a rental property. For example, the signal WEB may be configured to provide a rental request form interface for the web application  126 . A rental request may be communicated to the server computers  102   a - 102   n  as the signal RENTREQ. The rental request signal RENTREQ may provide a list attributes that the renter is seeking in a rental property. Details of the rental request web interface  126  may be described in association with  FIG.  9   . 
     The server computers  102   a - 102   n  may be configured to list rental properties based on the rental offers. For example, the servers  102   a - 102   n  may be configured to receive rental property offers from one or more property owners to provide the rental listings  120  that prospective renters may use to search using the signal RENTREQ. The server computers  102   a - 102   n  may provide the rental listings  120  (e.g., as the signal WEB) that prospective renters may browse through and/or may use the input signal RENTREQ from the rental request to find properties that closest match what the prospective renter is searching for according to the parameters entered into the web interface  126 . For example, the server computers  102   a - 102   n  may be configured to filter the available listings  120  based on the data provided in the signal RENTREQ (e.g., at a specific location, available at particular times, allows a particular number of guests, allows pets, etc.). For example, the server computers  102   a - 102   n  may provide a match-making service to enable property owners to find suitable renters and to enable prospective renters to find a suitable rental property. 
     Based on the rental offer signal RENTOFR and/or the rental request signal RENTREQ, the server computers  102   a - 102   n  may generate the rental agreement  122 . The rental agreement  122  may be communicated to both the property owner and the prospective renter as the signal RAGREE. The rental agreement  122  presented as the signal RAGREE may be a human-readable (e.g., text-based) contract that the property owner and renter may review and/or agree to. The rental agreement  122  may be generated from the selections made using the web interface  126  (e.g., based on the input from both the property owner and the prospective renter). Generally, the terms of the rental agreement  122  may be determined from the input of the property owner (e.g., using the signal RENTOFR). In some embodiments, the prospective renter may negotiate changes to the terms provided in the rental offer using the signal RENTREQ. Details of the rental agreement web interface  126  may be described in association with  FIG.  10   . 
     The property owner and the prospective renter may agree to the terms of the rental agreement  122 . The web interface  126  may enable the property renter to agree to the rental agreement  122  and the property owner user device  52   a  may communicate the signal CONFO to confirm and/or accept the rental agreement  122 . The web interface  126  may enable the prospective renter to agree to the rental agreement  122  and the renter user device  52   b  may communicate the signal CONFR to confirm and/or accept the rental agreement  122 . If the server computers  102   a - 102   n  receive the signal CONFO and the signal CONFR (e.g., both parties agree to the rental agreement  122 ), then the rental agreement  122  may be stored in the memory  112   a - 112   n  and the rental agreement  122  may be parsed by the detection engine  124 . If the server computers  102   a - 102   n  do not receive both the signal CONFO and the signal CONFR (e.g., at least one party does not confirm the rental agreement), then the rental agreement  122  may be discarded by the server computers  102   a - 102   n.    
     When the rental agreement  122  is agreed to, the server computers  102   a - 102   n  may be configured to generate a feature set for the camera systems  104   a - 104   n.  The detection engine  124  may be configured to parse the conditions of the rental agreement  122 . Based on the information from the rental agreement  122 , the detection engine  124  may generate a feature set for the particular camera(s)  104   a - 104   n  at the rental property  50  covered by the rental agreement  122 . The feature set may comprise the text of the rental agreement  122 , and/or information that the camera systems  104   a - 104   n  may use to generate parameters for performing object detection according to the rental agreement  122 . The feature set may be generated based on the conditions of the rental agreement  122 . In an example, the feature set may be a machine readable version (e.g., computer readable data) of the rental agreement  122  that may enable the camera systems  104   a - 104   n  to detect breaches of the rental agreement  122  using computer vision analysis and/or audio analysis. 
     The server computers  102   a - 102   n  may generate a signal (e.g., FEAT_SET). The signal FEAT_SET may provide the feature set to the camera systems  104   a - 104   n.  The feature set may be generated by the detection engine  124  in response to parsing the rental agreement  122 . The camera systems  104   a - 104   n  may operate (e.g., perform computer vision operations and/or audio analysis) based on the data in the signal FEAT_SET. For example, the signal FEAT_SET may provide parameters such as a number of people to detect, types of pets to detect, a maximum audio level, a time to search for particular features (e.g., additional guests may be allowed at particular times), a time range that the rental agreement  122  applies to, which features to ignore (e.g., do not detect faces to ensure privacy protection), etc. The type of information in the signal FEAT_SET may be varied according to the design criteria of a particular implementation. 
     The camera systems  104   a - 104   n  are shown comprising a block (or circuit)  130  and/or a block (or circuit)  132 . The circuit  130  may implement a processor. The circuit  132  may implement a memory. Each of the camera systems  104   a - 104   n  may implement the processor  130  and the memory  132 . The camera systems  104   a - 104   n  may each comprise other components (not shown). 
     Details of the components of the camera systems  104   a - 104   n  may be described in association with 
     The processor may receive the feature set from the server computers  102   a - 102   n.  The processor  130  may convert the feature set into detection parameters that may be used to perform the video and/or audio analysis. The detection parameters may be stored by the memory  132 . The detection parameters may provide criteria about what the camera systems  104   a - 104   n  should look for when analyzing video frames (e.g., number of people detected, detecting pets, detecting a noise level, etc.). 
     The camera systems  104   a - 104  may be configured to perform video surveillance during a normal (or default) mode of operation. In some embodiments, the camera systems  104   a - 104   n  may be configured to record (or store) video data and/or communicate video data when in the default mode of operation. For example, when the camera systems  104   a - 104   n  are not operating according to the feature set provided by the servers  102   a - 102   n  (e.g., the rental agreement  122  is not in effect), then the camera systems  104   a - 104   n  may operate in the default mode of operation. 
     When the camera systems  104   a - 104   n  are operating according to the feature set (e.g., during a time range that is covered by the rental agreement  122 ), the camera systems  104   a - 104   n  may not record the video/audio data capture and may not communicate the video/audio data. The video data and/or audio data may not leave (e.g., be communicated by) the camera systems  104   a - 104   n  to ensure privacy of the renter. The camera systems  104   a - 104   n  may perform the computer vision operations to extract data about the video frames (e.g., how many people are detected in a video frame, the type of pet detected, a current audio level, etc.). Once the data is extracted, the video frames and/or audio data may be discarded (or deleted, or overwritten, etc.). 
     The processor  130  may analyze the extracted data. The extracted data may be compared to the detection parameters from the feature set for the active rental agreement  122 . If the processor  130  detects a match of the data to the detection parameters (e.g., detects a scenario that is a breach of the rental agreement  122 ), the camera systems  104   a - 104   n  may generate a notification. A signal (e.g., NTF) is shown. The signal NTF may represent the notification generated by the camera systems  104   a - 104   n.  The notification NTF may be sent to the property owner and/or the renter. The notification NTF may indicate that the rental agreement  122  has been breached and/or the type of breach that has been detected. The notification may not provide the video frames and/or audio data that corresponds to the breach of the rental agreement  122  because the video frames and/or audio data may already be unavailable (e.g., deleted) to protect the privacy of the renter(s). 
     In the example shown, one feature set signal FEAT_SET is provided to each of the camera systems  104   a - 104   n.  However, in some embodiments, a different feature set may be generated for each camera system  104   a - 104   n.  For example, the feature set may be generated according to the specifications and/or detection capabilities of each camera system  104   a - 104   n  (e.g., different makes/models of cameras may be required to have a feature set in a different format). In another example, the feature set may be different based on particular rooms at the location  50  (e.g., the main living room may be allowed to have 10 guests and the camera system  104   a  in the main living room may receive a feature set to detect up to 10 guests, while a bedroom may be off limits to everyone and the camera system  104   b  in the bedroom may receive a feature set to detect a breach when any guest is detected). In yet another example, the feature set may be different for each of the camera systems  104   a - 104   n  based on the position of the camera systems  104   a - 104   n  in a room and/or a size of the room. For example, feature sets may be generated based on training data for small rooms and/or large rooms, depending on the height of the camera in the room, etc. In some embodiments, different pre-defined sets of data on the server computers  102   a - 102   n  may be downloaded into the camera systems  104   a - 104   n  based on the specifications of the camera systems  104   a - 104   n  and/or the characteristics of the room and/or mounting location of the camera systems  104   a - 104   n.  The variety of feature sets that may be provided to the camera systems  104   a - 104   n  may be defined by the rental agreement  122 . 
     The server computers  102   a - 102   n  may be configured to generate and/or receive a signal (e.g., QUERY). The signal QUERY maybe communicated to/from the camera systems  102   a - 102   n.  The signal QUERY may be communicated to enable the servers  102   a - 102   n  to determine the capabilities, features and/or limitations of the camera systems  104   a - 104   n.  The signal QUERY may be communicated to enable the servers  102   a - 102   n  to determine the characteristics of the room and/or environment that the camera systems  104   a - 104   n  are located in. In an example, the capabilities, features and/or limitations of the camera systems  104   a - 104   n  may comprise the type, number, and/or details of the object detection of the camera systems  104   a - 104   n  (e.g., how many objects may be detected, the types of objects that may be detected, the level of detail of the detection, whether a microphone is connected to receive audio, a height and/or distance from the objects to be detected, etc.). The capabilities of the camera systems  104   a - 104   n  may be used to determine what type of entries (e.g., terms and conditions) for the rental agreement  122  may be detectable by the camera systems  104   a - 104   n  at the location  50  and/or the types of feature sets to provide to the camera systems  104   a - 104   n.    
     The signal QUERY may be analyzed by the detection engine  124 . For example, the signal QUERY may comprise technical specifications, a make/model, etc. of the camera systems  104   a - 104   n.  The detection engine  124  may be configured to parse the technical specifications to determine what the camera systems  104   a - 104   n  may be capable of detecting. In some embodiments, the detection engine  124  may be configured to generate input fields for the web interface  126  in response to the signal QUERY. For example, if the camera systems  104   a - 104   n  are capable of detecting a type of pet using computer vision operations, then the detection engine  124  may generate an input field for the web interface  126  that enables input of a type of pet. Similarly, if the camera systems  104   a - 104   n  are not capable of detecting a type of pet using computer vision operations, then the input field for a type of pet may not be available on the web interface  126 . 
     Referring to  FIG.  2   , a diagram illustrating an example embodiment of the present invention is shown. An example rental scenario  150  is shown. The example rental scenario  150  may be at the location  50 . In an example, the location  50  may be a rental property in the listings  120 . In the example shown, the location  50  may be an interior location. In another example, the location  50  may be an outdoor location. In yet another example, the location  50  may be a combination of an indoor and outdoor location comprising multiple rooms of a building and/or multiple floors of a building. The type of location  50  may be varied according to the design criteria of a particular implementation. 
     In the example shown, the location  50  may comprise an interior wall  62 . An opening  64  is shown in the wall  62  (e.g., a doorway to another room). A number of people  70   a - 70   e  are shown. The person  70   a  is shown partially obscured by the wall  62 . The people  70   b - 70   e  may be visible in the opening  64 . 
     The camera system  104  is shown as a representative example of the camera systems  104   a - 104   n.  The camera system  104  may be configured to capture video data, capture audio data, perform computer vision operations and/or perform audio analytics. In an example, the camera system  104  may be configured to implement a rental property monitoring system using computer vision and audio analytics to detect parties and pets while preserving renter privacy. 
     Lines  152   a - 152   b  are shown. The lines  152   a - 152   b  may represent a field of view of the camera system  104 . The camera system  104  may capture video frames of the area within the field of view  152   a - 152   b.  In the example shown, the lines  152   a - 152   b  may provide an illustrative example of the field of view of the camera system  104 . In some embodiments, the camera system  104  may comprise one single 360-degree camera (e.g., capturing a 360-degree field of view). In some embodiments, the camera system  104  may comprise two back-to-back 180-degree cameras for capturing two 180-degree fields of view (e.g., in front and behind the camera system  104 ). In some embodiments, the camera system  104  may implement a fisheye lens providing a wide-angle field of view. The types of lenses used and/or the field of view captured by the camera system  104  may be varied according to the design criteria of a particular implementation. 
     In the example location  50 , the people  70   a - 70   e  may be within the field of view  152   a - 152   b  of the camera system  104 . In the example shown, the people  70   a - 70   e  may be visible in video frames captured by the camera system  104 . Dotted boxes  160   a - 160   e  are shown. The dotted boxes  160   a - 160   e  may represent objects detected by the camera system  104 . The dotted boxes  160   a - 160   e  are shown around the people  70   a - 70   e.  In the example shown, the camera system  104  may be configured to detect the people  70   a - 70   e  as the objects  160   a - 160   e.  While the people  70   a - 70   e  are shown as the detected objects  160   a - 160   e  in the example shown in association with  FIG.  2   , the detected objects  160   a - 160   e  may be animals and/or inanimate objects (e.g., furniture, electronics, structures, etc.). While five people  70   a - 70   e  are shown as the detected objects  160   a - 160   e,  the camera system  104  may be configured to detect any number of objects (e.g., detect the people  70   a - 70   n  as the objects  160   a - 160   n ). The number and/or types of objects  160   a - 160   n  detected by the camera system  104  may be varied according to the design criteria of a particular implementation. 
     A dotted box  162  is shown around the head of the person  70   c.  The dotted box  162  may represent the camera system  104  detecting characteristics of the object  160   c.  While the characteristics  162  are shown corresponding to the object  160   c,  the camera system  104  may be configured to detect the characteristics of each of the objects  160   a - 160   n.  The camera system  104  may be configured to analyze the characteristics  162  of the objects  160   a - 160   n  to determine what the objects  160   a - 160   n  are (e.g., classification), determine what the objects  160   a - 160   n  are doing (e.g., behavior analysis) and/or to distinguish one object from another object. The types of characteristics  162  detected and/or analyzed and/or the inferences made based on detecting the characteristics  162  may be varied according to the design criteria of a particular implementation. 
     The characteristics  162  may comprise descriptive and/or identifying attributes of the objects  160   a - 160   n.  In one example, the characteristics  162  may comprise clothing worn (e.g., style of clothing, a color of the clothing, the color of the pants, whether pants or shorts are worn, brand of pants worn, etc.). The characteristics  162  may comprise physical characteristics about the detected objects  160   a - 160   n.  In an example, the characteristics  162  may comprise a height, estimated weight, body type, hair color, skin color, gait, etc. The types of characteristics  162  detected may be used to distinguish one of the people  70   a - 70   n  from another of the people  70   a - 70   n  (e.g., to ensure that the number of people are counted without double-counting a person). The types of the characteristics  162  detected may be varied according to the design criteria of a particular implementation. 
     Waves  164   a - 164   b  are shown. The waves  164   a - 164   b  may represent audio detected by the camera system  104 . In the example shown, the waves  164   a - 164   b  may represent people talking. For example, the waves  164   a  may be an illustrative representation of the person  70   d  talking and the waves  164   b  may be an illustrative representation of the person  70   e  talking. The camera system  104  may be configured to determine an audio level (e.g., amplitude) of the audio  164   a - 164   b.  In the example shown, the audio  164   a - 164   b  may be generated from the people  70   a - 70   e.  The camera system  104  may be configured to determine an audio level of the location  50 . The audio level may comprise audio from the people  70   a - 70   e  (e.g., talking), televisions, music devices, objects breaking, etc. The type of audio detected may be varied according to the design criteria of a particular implementation. 
     The computer vision operations performed by the camera system  104  may be configured to extract data from the video frames and/or the audio captured. The extracted data may comprise parameters and/or statistics about the content of the video frames and/or audio captured. Extracted data  170   a - 170   e  is shown. In the example shown, the extracted data  170   a - 170   e  may represent a person count corresponding to the people  70   a - 70   e  in the location  50 . The extracted data  170   a - 170   e  may be used by the camera system  104  to make decisions. In one example, the decisions made by the camera system  104  based on the extracted data  170   a - 170   e  may comprise determining whether terms of the rental agreement  122  have been breached. After the extracted data  170   a - 170   e  has been generated from the video frames and/or the audio captured, the camera system  104  may discard the video frames and/or the audio captured (e.g., additional information from the video frames and/or the audio captured may be needed to make decisions). 
     In some embodiments, the computer vision operations performed by the camera system  104  may be configured to count a number of people at the location  50 . In the example shown, numbers 1-5 (e.g., the extracted data  170   a - 170   e ) are shown below a corresponding one of the detected objects  160   a - 160   e.  The camera system  104  may be configured to distinguish between the detected objects  160   a - 160   n  based on the characteristics  162  of each of the detected objects  160   a - 160   n.  For example, the camera system  104  may be configured to determine that the person  70   b  is one person and the person  70   c  is another person. In another example, the characteristics  162  may be used to prevent the people  70   a - 70   e  from being counted multiple times (e.g., distinguishing a reflection of a person from the actual person). 
     In the example shown, the person  70   a  is shown partially obscured behind the wall  62 . The camera system  104  may be configured to determine that the characteristics  162  of the visible portion of the person  70   a  corresponds to one person. For example, the camera system  104  may be configured to infer that a person has been detected based on a partial view. In the example shown, the hand of the person  70   d  is shown in front of the arm of the person  70   c.  The camera system  104  may be configured to determine that more than one person is present when one person is partially in front of another person. 
     In some embodiments, the camera system  104  may be configured to determine a behavior of the objects  160   a - 160   n.  Inferences may be made about the behavior of the objects  160   a - 160   n  based on the characteristics  162  detected. In an example, a person that is standing still and using arm gestures may be determined to be talking. In another example, regular or rhythmic body movement may be determined to be dancing. The body movement may be compared to the audio data (e.g., music) to determine whether the behavior indicates dancing. In one example, the camera system  104  may make an inference that there is a party at the location  50  based on the people  70   a - 70   n  dancing. In yet another example, the characteristics  162  may indicate that the people  70   a - 70   n  are holding drinks, which may indicate alcohol is being consumed at the location  50 . For example, optical character recognition (OCR) may be implemented to read labels (e.g., to detect beer, liquor, wine, etc.). 
     In the example shown, the characteristics  162  may correspond to a face of the person  70   c  (e.g., the detected object  160   c ). The characteristics  162  may be determined for each of the detected objects  160   a - 160   e  (e.g., the people  70   a - 70   e,  items held by the people  70   a - 70   e,  other items in the location  50 , etc.). The characteristics  162  may comprise a color of the detected objects  160   a - 160   e  (e.g., color of clothing worn). The characteristics  162  may comprise the size of objects (e.g., a height of a person). The characteristics  162  may comprise a classification of the detected objects  160   a - 160   e  (e.g., recognizing the people  70   a - 70   e  as distinct people, identifying an item as a television, recognizing an animal, etc.). In some embodiments, the characteristics  162  may be used by the camera system  104  to distinguish between the detected objects  160   a - 160   e.    
     The extracted data  170   a - 170   e  about the number of guests may be compared to the detection parameters converted by the processor  130  from the signal FEAT_SET. For example, the rental agreement  122  may indicate a limitation on the number of guests allowed at the rental property  50 . The detection engine  124  may convert the machine readable version of the rental agreement  122  into detection parameters that may be usable by the camera system  104  at the rental property  50  shown. The detection parameters may provide computer readable instructions about what types of objects and/or scenarios that the camera system  104  should detect at the rental property  50 . 
     For example, the rental agreement  122  may indicate a maximum of six guests, the detection engine  124  may query the camera system  104  to determine a format of the feature set for the camera system  104 , the detection engine  124  may convert the guest limitation from the rental agreement  122  into the feature set, and the processor  130  of the camera system  104  may convert the feature set into detection parameters used to perform the computer vision operations. If six guests is one of the detection parameters, then the camera system  104  may analyze video frames generated to count the number of the guests  70   a - 70   n  to determine if the number of guests is less than, equal to or greater than six guests. In the example shown, the extracted data  170   a - 170   e  about the number of the guests  70   a - 70   e  may indicate five guests are at the rental property  50 . Since the number of guests is less than the amount in the detection parameters, then the camera system  104  may not indicate a breach has been detected. If more than six guests were detected, then the camera system  104  may generate the notification signal NTF to indicate that a breach of the rental agreement  122  has been detected. 
     Generally, the feature set and/or conditions generated from the rental agreement  122  may comprise activities of the renter of the property  50 . In some embodiments, the detection parameters may comprise duties and/or requirements of the property owner. For example, when the property owner is preparing the property  50  for the renter, the camera system  104  may provide a check that the property has provided all the amenities agreed to in the rental agreement  122  (e.g., left out clean towels and clean bedsheets, left out toiletries, etc.). In some embodiments, the camera system  104  may be configured to detect particular events that the property owner has agreed to respond to in the rental agreement  122  (e.g., fixing a water leak, replacing a broken appliance, etc.). 
     In some embodiments, the computer vision operations may detect the type of activities being performed. For example, the rental agreement  122  may provide restriction on how the property  50  is allowed to be used. For example, if the property  50  has a pool and the rental agreement  122  does not permit usage of the pool, the camera system  104  maybe configured to detect if the renter(s) are using the pool in breach of the rental agreement  122 . 
     Referring to  FIG.  3   , a diagram illustrating monitoring rental properties using computer vision and/or audio analytics is shown. An example scenario  180  is shown. The example scenario  180  may comprise a number of locations  50   a - 50   n,  the network/cloud service  102  and/or the remote device  52   i.  In some embodiments, the locations  50   a - 50   n  may be remote locations (e.g., different geographic locations such as different cities, different countries, different areas within the same city, etc.). In some embodiments, the locations  50   a - 50   n  may be different locations within the same building (e.g., different rooms of a rental property, an interior and an exterior of a rental property, etc.). Each of the locations  50   a - 50   n  are shown implementing a respective camera system  104  (e.g., the camera systems  104   a - 104   n ). For example, each of the camera systems  104   a - 104   n  may be configured to operate independently of each other (e.g., video data and/or audio data may not be shared). However, the parameters and statistics generated by the camera systems  104   a - 104   n  may be shared. Generally, each of the camera systems  104   a - 104   n  may be configured to monitor the respective locations  50   a - 50   n  similar to the camera system  104  monitoring the location  50  described in association with  FIG.  2   . 
     The network  102  may be implemented as part of a cloud computing platform (e.g., distributed computing). In an example, the network  102  may be implemented as a group of cloud-based, scalable server computers. By implementing a number of scalable servers, additional resources (e.g., power, processing capability, memory, etc.) may be available to process and/or store variable amounts of data. For example, the network  102  may be configured to scale (e.g., provision resources) based on demand. The network  102  may implement scalable computing (e.g., cloud computing). The scalable computing may be available as a service to allow access to processing and/or storage resources without having to build infrastructure. In some embodiments, the network  102  may be configured to provide resources such as training data and/or a database of feature maps (e.g., feature maps of recognized objects to perform object recognition and/or classification). For example, the training data and/or feature maps may be communicated to the camera systems  104   a - 104   n  by the network  102  based on the contents of the rental agreement  122 . 
     In the example shown, the remote device  52   i  may be implemented as a smartphone. In the example shown, one remote device  52   i  is shown. However, the example scenario  180  may be implemented having multiple remote devices (e.g., remote devices  52   a - 52   n,  not shown). In an example, the smartphone  52   a  may be used by the landlord and the smartphone  52   b  may be used by the renter. 
     The remote device  52   i  may be configured to receive notifications from the camera systems  104   a - 104   n.  The camera systems  104   a - 104   n  may not communicate the video data and/or the audio data. For example, the camera systems  104   a - 104   n  may discard video data and/or audio data after performing the analysis. Discarding the video data and/or audio data may ensure the privacy of the people  70   a - 70   n.  The notification may provide information that corresponds to the determinations made by the camera systems  104   a - 104   n  in response to performing the analysis of the video data and/or audio data (e.g., based on the statistic and parameters). 
     In the example shown, the remote device  52   i  is shown at a location other than the locations  50   a - 50   n.  For example, the notification may provide the rental property owner information about the locations  50   a - 50   n  from a remote geographic location. In some embodiments, the remote device  52   i  may be located at or near one of the locations  50   a - 50   n.  For example, to further protect privacy of the people  70   a - 70   n,  the information about the analysis performed by the camera systems  104   a - 104   n  may be stored on-site at the locations  50   a - 50   n  and the rental property owner may access the information on-site. 
     The locations  50   a - 50   n  may each comprise a respective one of the camera systems  104   a - 104   n.  Each of the camera systems  104   a - 104   n  are shown having the field of view  152   a - 152   b.  In the example shown, the locations  50   a - 50   n  may be the subject of the monitoring. 
     The rental property owner may provide the people  70   a - 70   n  with the rental agreement  122 . The rental agreement  122  may comprise a list of restrictions. The restrictions may comprise various entries that may comprise a number of people, disallowed animals, noise levels and/or behaviors. The camera systems  104   a - 104   n  may be configured to perform the analysis of the video data and/or the audio data to determine whether the data detected matches any entries on the list of restrictions. For example, the list of restrictions may be converted to parameters that may be used by the computer vision operations and/or the audio analytics to perform the detection. If the data detected by the camera systems  104   a - 104   n  matches any of the entries on the list of restrictions, the camera system  104  may generate a notification. The notification may be a warning to the people  70   a - 70   n  to cure the cause of the warning. The notification may be provided to the rental property owner. 
     Status signals (e.g., STAT_A-STAT_N) are shown presented to the network  102 . The status signals STAT_A-STAT_N may be generated by the camera systems  104   a - 104   n.  The status signals STAT_A-STAT_N may represent the notifications generated by the camera systems  104   a - 104   n.  In some embodiments, the status signals STAT_A-STAT_N may provide continual updates (e.g., provided even if the camera system  104   a - 104   n  does not detect any of the entries of the list of restrictions). For example, the signals STAT_A-STAT_N may provide continual updates about the extracted data  170   a - 170   e  (e.g., the number of the people  70   a - 70   n  at the rental property) generated by the camera systems  104   a - 104   n.    
     In the example shown, the signals STAT_A-STAT_N are shown communicated from the camera systems  104   a - 104   n  to the network  102 . In some embodiments, the signals STAT_A-STAT_N may be shared between the camera systems  104   a - 104   n.  For example, the breach of the rental agreement  122  may be based on a total number of people detected. If the locations  50   a - 50   n  are multiple rooms in a single rental property, one of the camera systems  104   a - 104   n  alone may not be capable of counting all the people at once. By sharing the parameters and statistics in the signals STAT_A-STAT_N, the cameras  104   a - 104   n  may determine the total number of people. 
     The signal NTF is shown. The signal NTF may be presented to the remote device  52   i  in response to one or more of the status signals STAT_A-STAT_N. The signal NTF may be provided in a format readable by the remote device  52   i.  For example, an application may be provided for the rental property owner to use on the remote device  52   i  and the application may be compatible with an API of the network  102  and/or the camera systems  104   a - 104   n.    
     In the example shown, the notification signal NTF is shown provided by the network  102  to the remote device  52   i.  In some embodiments, the camera systems  104   a - 104   n  may be configured to generate the notification locally. For example, the camera systems  104   a - 104   n  may comprise a speaker configured to provide an audio warning to the renters when a breach has been detected (e.g., “The terms of the rental agreement  122  have been breached. Ten people have been detected and only 4 are allowed. Please ask 6 people to leave.”). 
     In the location  50   a,  three of the people  70   a - 70   c  are shown. The camera system  104   a  may be configured to count the number of people at the location  50   a  using the computer vision operations. The computer vision operations performed by the camera system  104   a  may detect three people. The example shown in association with the location  50   a  may represent detecting more people than are allowed by the list of restrictions. For example, the rental agreement  122  may provide a restriction of two people. In an example, when three people are detected by the camera system  104   a,  the camera system  104   a  may generate the signal STAT_A. In another example, the camera system  104   a  may provide regular updates about the number of people using the signal STAT_A (e.g., the camera system  104   a  may indicate that two people are at the location  50   a,  then update at regular intervals and when the third person arrives the number of people will be updated). The signal NTF may be presented by the network  102  to the remote device  52   i  in response to the signal STAT_A. In an example, the notification may indicate that the entry on the list of restrictions for number of people has been violated. 
     In the location  50   b,  the person  70   d  and a speaker  74  are shown. The camera system  104   b  may be configured to count the number of people at the location  50   a  using the computer vision operations. In an example, the computer vision operations performed by the camera system  104   b  may detect one person, which may be in compliance with the list of restrictions. The example shown in association with the location  50   b  may represent detecting a higher audio level than is allowed by the list of restrictions. For example, the rental agreement  122  may provide a restriction on noise level. In the example shown, the speaker  74  may be set to eleven (e.g., too loud compared to the noise level threshold from the list of restrictions). The camera system  104   b  may perform the analysis of the audio from the speaker  74  and determine whether the amplitude of the audio detected matches (or is greater than) the audio amplitude limitation level entry on the list of restrictions. In one example, when the camera system  104   b  detects that the audio level is greater than allowed by the list of restrictions, the camera system  104   b  may generate the signal STAT_B. In another example, the camera system  104   b  may provide regular updates about the detected audio level using the signal STAT_B (e.g., the camera system  104   b  may indicate the audio level in decibels and then update at regular intervals). The signal NTF may be presented by the network  102  to the remote device  52   i  in response to the signal STAT_B. In an example, the notification may indicate that the entry on the list of restrictions for noise level has been violated. 
     In the location  50   n,  the person  70   n  and a bed  76  are shown. The camera system  104   n  may be configured to count the number of people at the location  50   n  using the computer vision operations. In an example, the computer vision operations performed by the camera system  104   n  may detect one person, which may be in compliance with the list of restrictions. The camera system  104   n  may further perform the audio analysis to detect the noise level. In the example shown, the person  70   n  may be sleeping and not making noise. Since the person  70   n  may be in compliance with the list of restrictions, the camera system  104   n  may not send the signal STAT_N. In some embodiments, the camera system  104   n  may send the signal STAT_N that shows the number of people and the audio level is in compliance with the list of restrictions. 
     In some embodiments, the signals STAT_A-STAT_N may not provide details of the behavior of the people  70   a - 70   n  to preserve privacy. For example, the signal STAT_N may not indicate that the person  70   n  is sleeping in the bed  76 . The signals STAT_A-STAT_N may merely provide sufficient information to indicate whether or not the renters (e.g., the people  70   a - 70   n ) are in compliance with the list of restrictions. For example, the signal STAT_N may provide information that the number of people detected and the noise level was less than the threshold indicated by the list of restrictions in the rental agreement  122 . 
     In some embodiments, the camera systems  104   a - 104   n  may be configured to share data. The data shared between the camera systems  104   a - 104   n  may enable additional information to be aggregated for inferences to be made by the camera systems  104   a - 104   n.  For example, if the camera systems  104   a - 104   n  provide video data and audio data from multiple rooms of the same rental property, the camera systems  104   a - 104   n  may share the number of people (e.g., the extracted data  170   a - 170   e ) counted in each room to determine a total number of people at the rental property. To ensure privacy, the camera systems  104   a - 104   n  may not share the video data and/or the audio data. To ensure privacy, the camera systems  104   a - 104   n  may share the results determined by the computer vision operations and/or the audio analysis. For example, each camera system  104   a - 104   n  may perform the computer vision operations to determine the number count  170   a - 170   e  of people and share the number count  170   a - 170   e  to determine a total number of occupants at the rental property. The results shared may be limited to ensure privacy while providing enough information to make a determination about whether the terms of the rental agreement  122  have been breached. For example, the number count  170   a - 170   e  may be shared if the number of people is an entry of the rental agreement  122 , but may not be shared if the number of people is not an entry of the rental agreement  122 . 
     The camera systems  104   a - 104   n  may be configured to apply various types of audio analysis (e.g., sound levels, frequency analysis, artificial intelligence for detecting words and/or phrases, etc.). In an example, the camera systems  104   a - 104   n  may be configured to determine a sound level of the audio and/or detect the type of audio (e.g., determine whether the detected audio is loud music, a loud animal, loud voices, etc.). In some embodiments, the camera systems  104   a - 104   n  may be configured to combine the audio analysis with video analysis (e.g., perform the computer vision analysis to determine the body movements to determine that the people  70   a - 70   n  are dancing and perform the audio analysis to determine that there is loud music). The type of analysis performed may be based on the list of restrictions. For example, merely playing music too loudly may be an issue and the notification may be sent based on the sound level without performing the behavioral analysis to determine that the people  70   a - 70   n  are dancing (e.g., the sound level may be an issue regardless of what the people  70   a - 70   n  are doing). 
     In some embodiments, the camera systems  104   a - 104   n  may be implemented as small, discreet cameras that may be hidden from view. Hiding the camera systems  104   a - 104   n  may prevent renters from attempting to obscure the lens. In some embodiments, the camera systems  104   a - 104   n  may be implemented in plain view and/or highlighted to bring attention to the camera systems  104   a - 104   n.  The camera systems  104   a - 104   n  maybe configured to detect when the field of view  152   a - 152   n  has been obscured (e.g., detect all black video frames, detect if more than a threshold percentage of the video frame is dark, etc.). For an example of a rental property, the camera systems  104   a - 104   n  may be explicitly described in the rental agreement  122 , along with the locations of the camera systems  104   a - 104   n  and the list of restrictions. Implementing the camera systems  104   a - 104   n  such that the camera systems  104   a - 104   n  are visible may act as a deterrent to those seeking to rent accommodation and hold parties. In an example, when the renter fills out the rental application form there may be a request to acknowledge that the camera systems  104   a - 104   n  are installed and state how many people will be present and whether there will be any pets. 
     The camera systems  104   a - 104   n  may maintain the privacy of the renter. No video data and/or audio data may be streamed or recorded. The computer vision operations may be configured to detect a crowd and/or count people. The camera systems  104   a - 104   n  may be configured to detect if the field of view  152   a - 152   n  has been obscured in any way. In some embodiments, the camera systems  104   a - 104   n  may not make determinations based on sex, race, other physical features, etc. In one example, the camera systems  104   a - 104   n  may be configured to detect when each of the people  70   a - 70   n  first arrive and then compare the people count  170   a - 170   e  to a threshold (e.g., based on the entry in the list of restrictions). For example, the camera systems  104   a - 104   n  may determine whether a party is being held at the rental property based on various parameters (e.g., people count, loud noises, music, etc.). 
     The camera systems  104   a - 104   n  may be configured to detect loud sounds (e.g., identifying music, breaking glass, smoke alarms, etc.). The camera systems  104   a - 104   n  may be configured to detect pets (e.g., cats, dogs, birds, ferrets, snakes, gerbils, etc.). The camera systems  104   a - 104   n  may be configured to count people to determine if more people are detected than the threshold number of people that may be set by the landlord. The list of restrictions may be provided to the renters in the rental agreement  122 . The list of restrictions may be converted to threshold parameters that may be readable by the camera systems  104   a - 104   n.  The types of conditions that the camera systems  104   a - 104   n  may search for using the computer vision operations and/or the audio analytics may be determined based on the threshold parameters. In an example, if the landlord does not list pets as an entry on the list of restrictions, the computer vision operations may not search for pets. The method of converting the list of restrictions to the threshold parameters may be varied according to the design criteria of a particular implementation. 
     In some embodiments, the camera systems  104   a - 104   n  may be configured to implement computer vision acceleration hardware to perform the computer vision operations. In some embodiments, the camera systems  104   a - 104   n  may be configured to perform cropping and/or zooming techniques as part of the computer vision operations to assist in the person and pet detection. 
     In some embodiments, the camera systems  104   a - 104   n  may be configured to implement facial recognition. Facial recognition may represent a trade-off between available types of detections that may be made for the list of restrictions and privacy concerns. In an example, the camera systems  104   a - 104   n  may be configured to perform the facial recognition locally (e.g., comparing feature maps from the faces of previously detected people to the features maps currently detected on the people  70   a - 70   n  in the location  50 ). However, a database of feature maps to compare against may comprise a large amount of data (e.g., typically stored using services such as the cloud services  102 ). Sending the currently detected feature maps for facial recognition may be a potential privacy concern since the data detected by the camera systems  104   a - 104   n  would not all be kept locally and then discarded. In some embodiments, depending on the storage capacity available to the camera systems  104   a - 104   n  a number of feature maps for faces may be stored locally. In an example, parents may want a limited number of people restricted (e.g., detect a boyfriend/girlfriend when the parents are not home). In some embodiments, particular characteristics  162  may be detected (e.g., hair color, color of clothing, type of clothing worn, etc.). In some embodiments, the processor  130  of the camera systems  104   a - 104   n  may be capable of performing facial recognition, but may not be configured to perform the facial recognition. In an example, in the default mode of operation (e.g., when the rental agreement  122  is not active), the facial recognition may be performed, and facial recognition may be deactivated when the rental agreement  122  is active. 
     The signal NTF may be customizable by the landlord. In an example, the landlord may receive a notification on the smartphone  52   i  when music is detected above the threshold level set by the list of restrictions. In some embodiments, the renter may also receive the notification. For example, if the renter also receives the notification, the renter may have an opportunity to correct the issue (e.g., turn down the music) before the landlord has to intervene. In some embodiments, the notification may provide details of the entry on the list of restrictions that has been violated (e.g., “five people have been detected but only two people are allowed on the property”). Providing a detailed notification may enable the renter to take action to ensure that the property is protected according to the list of restrictions. In some embodiments, the notification may be configured to protect privacy by not indicating the particular entry on the list of restrictions that has been violated. For example, the notification may provide the list of restrictions and indicate that a violation has been detected. In some embodiments, the renter may be able to respond to the notification. The response to the notification may be sent to the landlord (e.g., to acknowledge the notification and confirm they have taken action to correct the violation). 
     The camera systems  104   a - 104   n  may be configured to discard the video data after performing the computer vision operations. The video data may not be stored long term. The video data may not be streamed to a remote location. In an example, to perform the computer vision operations, the camera systems  104   a - 104   n  may perform the video analysis on a single video frame. Some additional information from data in a buffer maybe used. The camera systems  104   a - 104   n  may generate the data (e.g., conclusions, inferences, the number of people  70   a - 70   e,  etc.), and the video data may be discarded. The data may be updated as new data is extracted from incoming video frames. However, previous video data is unavailable after being discarded. The previously determined data may be used and/or stored but the video data and/or audio data that the data was extracted from may be discarded. In an example, a history of the data may be stored. For example, the noise level may be recorded along with a time stamp of when the noise level was determined. The history of the data may be compared with complaints. For example, if a neighbor calls in a noise complaint with the police, the camera systems  104   a - 104   n  may provide the timestamped noise level to determine whether the neighbor has provided a legitimate complaint. 
     Referring to  FIG.  4   , a block diagram illustrating an example embodiment of the camera system is shown. A block diagram of the camera system  104   i  is shown. The camera system  104   i  may be a representative example of the camera system  104   a - 104   n  shown in association with  FIGS.  1 - 3   . The camera system  104   i  generally comprises the processor  130 , the memory  132 , blocks (or circuits)  200   a - 200   n,  blocks (or circuits)  202   a - 202   n,  a block (or circuit)  204 , blocks (or circuits)  206   a - 206   n  and/or blocks (or circuits)  208   a - 208   n.  The blocks  200   a - 200   n  may implement lenses. The circuits  202   a - 202   n  may implement capture devices. The circuit  204  may implement a communication device. The circuits  206   a - 206   n  may implement microphones (e.g., audio capture devices). The circuits  208   a - 208   n  may implement audio output devices (e.g., speakers). The camera system  104   i  may comprise other components (not shown). In the example shown, some of the components  200 - 208  are shown external to the camera system  104   i.  However, the components  200 - 208  may be implemented within and/or attached to the camera system  104   i  (e.g., the speakers  208   a - 208   n  may provide better functionality if not located inside a housing of the camera system  104   i ). The number, type and/or arrangement of the components of the camera system  104   i  may be varied according to the design criteria of a particular implementation. 
     In an example implementation, the circuit  130  may be implemented as a video processor. The processor  130  may comprise inputs  220   a - 220   n  and/or other inputs. The processor  130  may comprise an input/output  222 . The processor  130  may comprise an output  224   a  and an input  224   b.  The processor  130  may comprise an input  226 . The processor  130  may comprise an output  228  and/or other outputs. The number of inputs, outputs and/or bi-directional ports implemented by the processor  130  may be varied according to the design criteria of a particular implementation. 
     In the embodiment shown, the capture devices  202   a - 202   n  may be components of the camera system  104   i.  In some embodiments, the capture devices  202   a - 202   n  may be separate devices (e.g., remotely connected to the camera system  104   i,  such as a drone, a robot and/or a system of security cameras configured capture video data) configured to send data to the camera system  104   i.  In one example, the capture devices  202   a - 202   n  may be implemented as part of an autonomous robot configured to patrol particular paths such as hallways. Similarly, in the example shown, the wireless communication device  204 , the microphones  206   a - 206   n  and/or the speakers  208   a - 208   n  are shown external to the camera system  104   i  but in some embodiments may be a component of (e.g., within) the camera system  104   i.    
     The camera system  104   i  may receive one or more signals (e.g., IMF_A-IMF_N), the signal FEAT_SET and/or one or more signals (e.g., DIR_AUD). The camera system  104   i  may present the signal STAT (e.g., one of the signals STAT_A-STAT_N shown in association with  FIG.  3   ) and/or a signal (e.g., DIR_AOUT). The capture devices  202   a - 202   n  may receive the signals IMF_A-IMF_N from the corresponding lenses  200   a - 200   n.  The processor  130  may receive the signal DIR_AUD from the microphones  206   a - 206   n.  The processor  130  may present the signal STAT to the communication device  204  and receive the signal FEAT_SET from the communication device  204 . For example, the wireless communication device  204  may be a radio-frequency (RF) transmitter. In another example, the communication device  204  may be a Wi-Fi module. In another example, the communication device  204  may be a device capable of implementing RF transmission, Wi-Fi, Bluetooth and/or other wireless communication protocols. The processor  130  may present the signal DIR_AOUT to the speakers  208   a - 208   n.    
     The lenses  200   a - 200   n  may capture signals (e.g., IM_A-IM_N). The signals IM_A-IM_N may be an image (e.g., an analog image) of the environment near the camera system  104   i  presented by the lenses  200   a - 200   n  to the capture devices  202   a - 202   n  as the signals IMF_A-IMF_N. The lenses  200   a - 200   n  may be implemented as an optical lens. The lenses  200   a - 200   n  may provide a zooming feature and/or a focusing feature. The capture devices  202   a - 202   n  and/or the lenses  200   a - 200   n  may be implemented, in one example, as a single lens assembly. In another example, the lenses  200   a - 200   n  may be a separate implementation from the capture devices  202   a - 202   n.  The capture devices  202   a - 202   n  are shown within the circuit  104   i.  In an example implementation, the capture devices  202   a - 202   n  maybe implemented outside of the circuit  104   i  (e.g., along with the lenses  200   a - 200   n  as part of a lens/capture device assembly). 
     The capture devices  202   a - 202   n  may be configured to capture image data for video (e.g., the signals IMF_A-IMF_N from the lenses  200   a - 200   n ). In some embodiments, the capture devices  202   a - 202   n  maybe video capturing devices such as cameras. The capture devices  202   a - 202   n  may capture data received through the lenses  200   a - 200   n  to generate raw pixel data. In some embodiments, the capture devices  202   a - 202   n  may capture data received through the lenses  200   a - 200   n  to generate bitstreams (e.g., generate video frames). For example, the capture devices  202   a - 202   n  may receive focused light from the lenses  200   a - 200   n.  The lenses  200   a - 200   n  may be directed, tilted, panned, zoomed and/or rotated to provide a targeted view from the camera system  104   i  (e.g., to provide coverage for a panoramic field of view such as the field of view  152   a - 152   b ). The capture devices  202   a - 202   n  may generate signals (e.g., PIXELD_A-PIXELD_N). The signals PIXELD_A-PIXELD_N may be pixel data (e.g., a sequence of pixels that may be used to generate video frames). In some embodiments, the signals PIXELD_A-PIXELD_N may be video data (e.g., a sequence of video frames). The signals PIXELD_A-PIXELD_N may be presented to the inputs  220   a - 220   n  of the processor  130 . 
     The capture devices  202   a - 202   n  may transform the received focused light signals IMF_A-IMF_N into digital data (e.g., bitstreams). In some embodiments, the capture devices  202   a - 202   n  may perform an analog to digital conversion. For example, the capture devices  202   a - 202   n  may perform a photoelectric conversion of the focused light received by the lenses  200   a - 200   n.  The capture devices  202   a - 202   n  may transform the bitstreams into pixel data, images and/or video frames. In some embodiments, the pixel data generated by the capture devices  202   a - 202   n  may be uncompressed and/or raw data generated in response to the focused light from the lenses  200   a - 200   n.  In some embodiments, the output of the capture devices  202   a - 202   n  may be digital video signals. 
     The communication device  204  may send and/or receive data to/from the camera system  104   i.  In some embodiments, the communication device  204  may be implemented as a wireless communications module. In some embodiments, the communication device  204  may be implemented as a satellite connection to a proprietary system. In one example, the communication device  204  may be a hard-wired data port (e.g., a USB port, a mini-USB port, a USB-C connector, HDMI port, an Ethernet port, a DisplayPort interface, a Lightning port, etc.). In another example, the communication device  204  may be a wireless data interface (e.g., Wi-Fi, Bluetooth, ZigBee, cellular, etc.). 
     The communication device  204  may be configured to receive the signal FEAT_SET from the network  102 . The signal FEAT_SET may comprise a feature set that corresponds to the rental agreement  122 . The feature set information may comprise instructions for the processor  130  for determining a breach of the rental agreement  122 . Details of the feature set information may be described in association with  FIG.  5   . 
     The processor  130  may receive the signals PIXELD_A-PIXELD_N from the capture devices  202   a - 202   n  at the inputs  220   a - 220   n.  The processor  130  may send/receive a signal (e.g., DATA) to/from the memory  132  at the input/output  222 . The processor  130  may send the signal STAT to the communication device  204  via the output port  224   a.  In some embodiments, the port  224   a  may be an input/output port and the processor  130  may receive one of the signals STAT_A-STAT_N from the other camera systems  104   a - 104   n.  The processor  130  may receive the signal FEAT_SET from the communication device  204  via the input port  224   b.  The processor  130  may receive the signal DIR_AUD from the microphones  206   a - 206   n  at the port  226 . The processor  130  may send the signal DIR_AOUT to the speakers  208   a - 208   n  via the port  228 . In an example, the processor  130  may be connected through a bi-directional interface (or connection) to the capture devices  202   a - 202   n,  the communication device  204 , the memory  132 , the microphones  206   a - 206   n  and/or the speakers  208   a - 208   n.  The processor  130  may store and/or retrieve data from the memory  132 . The memory  132  may be configured to store computer readable/executable instructions (or firmware). The instructions, when executed by the processor  130 , may perform a number of steps. 
     The signal PIXELD_A-PIXELD_N may comprise raw pixel data providing a field of view captured by the lenses  200   a - 200   n.  The processor  130  may be configured to generate video frames from the pixel data PIXELD_A-PIXELD_N. The video frames generated by the processor  130  may be used internal to the processor  130 . In some embodiments, the video frames may be communicated to the memory  132  for temporary storage. Generally, the video frames generated by the processor  130  may not leave the processor  130 . The processor  130  may be configured to discard the video frames generated. 
     The processor  130  may be configured to make decisions based on analysis of the video frames generated from the signals PIXELD_A-PIXELD_N. The processor  130  may generate the signal STAT, the signal DATA, the signal DIR_AOUT and/or other signals (not shown). The signal STAT, the signal DATA and/or the signal DIR_AOUT may each be generated (in part) based on one or more decisions made and/or functions performed by the processor  130 . The decisions made and/or functions performed by the processor  130  may be determined based on data received by the processor  130  at the inputs  220   a - 220   n  (e.g., the signals PIXELD_A-PIXELD_N), the input  222 , the input  224   b,  the input  226  and/or other inputs. 
     The inputs  220   a - 220   n,  the input/output  222 , the output  224   a,  the input  224   b,  the input  226 , the output  220  and/or other inputs/outputs may implement an interface. The interface may be implemented to transfer data to/from the processor  130 , the communication device  204 , the capture devices  202   a - 202   n,  the memory  132 , the microphones  206   a - 206   n,  the speakers  208   a - 208   n  and/or other components of the camera system  104   i.  In one example, the interface may be configured to receive (e.g., via the inputs  220   a - 220   n ) the pixel data signals PIXELD_A-PIXELD_N each from a respective one of the capture devices  202   a - 202   n.  In another example, the interface may be configured to receive (e.g., via the input  226 ) the directional audio DIR_AUD. In yet another example, the interface may be configured to transmit parameters and/or statistics about the video frames (e.g., the signal STAT) and/or the converted data determined based on the computer vision operations to the communication device  204 . In still another example, the interface may be configured to receive the feature set information FEAT_SET (e.g., via the input port  224   b ) from the communication device  204 . In another example, the interface may be configured to transmit directional audio output (e.g., the signal DIR_AOUT) to each of the speakers  208   a - 208   n.  The interface may be configured to enable transfer of data and/or translate data from one format to another format to ensure that the data transferred is readable by the intended destination component. In an example, the interface may comprise a data bus, traces, connectors, wires and/or pins. The implementation of the interface may be varied according to the design criteria of a particular implementation. 
     The signal STAT may be presented to the communication device  204 . In some embodiments, the signal STAT may comprise parameters and/or statistics determined by the processor  130  about the video frames. The signal STAT may be generated in response to the computer vision operations performed. The video frames may be encoded, cropped, stitched and/or enhanced versions of the pixel data received from the signals PIXELD_A-PIXELD_N. In an example, the video frames maybe a high resolution, digital, encoded, de-warped, stabilized, cropped, blended, stitched and/or rolling shutter effect corrected version of the signals PIXELD_A-PIXELD_N. 
     In some embodiments, the signal STAT may be a text message (e.g., a string of human readable characters). In some embodiments, the signal STAT may be a symbol that indicates an event or status (e.g., sound symbol indicating loud noise has been detected, an animal symbol indicating a pet has been detected, a symbol of a group of people to indicate that too many people have been detected at the location  50 , etc.). The signal STAT may be generated based on video analytics (e.g., computer vision operations) performed by the processor  130  on the video frames generated from the pixel data PIXELD_A-PIXELD_N. The processor  130  may be configured to perform the computer vision operations to detect objects and/or events in the video frames and then convert the detected objects and/or events into statistics and/or parameters. The data determined by the computer vision operations maybe converted to the human-readable format by the processor  130 . The data from the computer vision operations that has been converted to the human-readable format may be communicated as the signal STAT. 
     In some embodiments, the signal STAT may be data generated by the processor  130  (e.g., video analysis results, audio/speech analysis results, etc.) that may be communicated to a cloud computing service in order to aggregate information and/or provide training data for machine learning (e.g., to improve object detection, to improve audio detection, etc.). The type of information communicated by the signal STAT may be varied according to the design criteria of a particular implementation. 
     The circuit  104   i  may implement a camera system. In some embodiments, the camera system  104   i  may be implemented as a drop-in solution (e.g., installed as one component). In an example, the camera system  104   i  may be a device that may be installed as an after-market product (e.g., a retro-fit for a drone, a retro-fit for a security system, etc.). In some embodiments, the camera system  104   i  may be a component of a security system. The number and/or types of signals and/or components implemented by the camera system  104   i  may be varied according to the design criteria of a particular implementation. 
     The video data of the targeted view captured in the field of view  152   a - 152   b  may be generated from the signals/bitstreams/data PIXELD_A-PIXELD_N. The capture devices  202   a - 202   n  may present the signals PIXELD_A-PIXELD_N to the inputs  220   a - 220   n  of the processor  130 . The signals PIXELD_A-PIXELD_N may be used by the processor  130  to generate the video frames/video data. In some embodiments, the signals PIXELD_A-PIXELD_N maybe video streams captured by the capture devices  202   a - 202   n.  In some embodiments, the capture devices  202   a - 202   n  may be implemented in the camera system  104   i.  In some embodiments, the capture devices  202   a - 202   n  may be configured to add to existing functionality to the camera system  104   i.    
     Each of the capture devices  202   a - 202   n  may comprise a block (or circuit)  230 , a block (or circuit)  232 , and/or a block (or circuit)  234 . The circuit  230  may implement a camera sensor (e.g., a complementary metal-oxide-semiconductor (CMOS) sensor). The circuit  232  may implement a camera processor/logic. The circuit  234  may implement a memory buffer. As a representative example, the capture device  202   a  is shown comprising the sensor  230   a,  the logic block  232   a  and the buffer  234   a.  Similarly, the capture devices  202   b - 202   n  may comprise the camera sensors  230   b - 230   n,  the logic blocks  232   b - 232   n  and the buffers  234   b - 234   n.  The sensors  230   a - 230   n  may each be configured to receive light from the corresponding one of the lenses  200   a - 200   n  and transform the light into digital data (e.g., the bitstreams). 
     In one example, the sensor  230   a  of the capture device  202   a  may receive light from the lens  200   a.  The camera sensor  230   a  of the capture device  202   a  may perform a photoelectric conversion of the light from the lens  200   a.  In some embodiments, the sensor  230   a  may be an oversampled binary image sensor. The logic  232   a  may transform the bitstream into a human-legible content (e.g., pixel data and/or video data). For example, the logic  232   a  may receive pure (e.g., raw) data from the camera sensor  230   a  and generate pixel data based on the raw data (e.g., the bitstream). The memory buffer  234   a  may store the raw data and/or the processed bitstream. For example, the frame memory and/or buffer  234   a  may store (e.g., provide temporary storage and/or cache) the pixel data and/or one or more of the video frames (e.g., the video signal). 
     The microphones  206   a - 206   n  may be configured to capture incoming audio and/or provide directional information about the incoming audio. Each of the microphones  206   a - 206   n  may receive a respective signal (e.g., AIN_A-AIN_N). The signals AIN_A-AIN_N may be audio signals from the environment  50  near the camera system  104   i.  For example, the signals AIN_A-AIN_N may be ambient noise in the environment  50  and/or the audio  164   a - 164   n  from the subjects  70   a - 70   n.  The microphones  206   a - 206   n  may be configured to generate the signal DIR_AUD in response to the signals AIN_A-AIN_N. The signal DIR_AUD may be a signal that comprises the audio data from the signals AIN_A-AIN_N. The signal DIR_AUD may be a signal generated in a format that provides directional information about the signals AIN_A-AIN_N. 
     The microphones  206   a - 206   n  may provide the signal DIR_AUD to the interface  226 . The camera system  104   i  may comprise the interface  226  configured to receive data (e.g., the signal DIR_AUD) from one or more of the microphones  206   a - 206   n.  In one example, data from the signal DIR_AUD presented to the interface  226  may be used by the processor  130  to determine the location of the source of the audio  164   a - 164   n.  In another example, the microphones  206   a - 206   n  may be configured to determine the location of the audio  164   a - 164   n  and present the location to the interface  226  as the signal DIR_AUD. 
     The number of microphones  206   a - 206   n  may be varied according to the design criteria of a particular implementation. The number of microphones  206   a - 206   n  may be selected to provide sufficient directional information about the incoming audio (e.g., the number of microphones  206   a - 206   n  implemented may be varied based on the accuracy and/or resolution of directional information acquired). In an example, 2 to 6 of the microphones  206   a - 206   n  may be implemented. In some embodiments, an audio processing component may be implemented with the microphones  206   a - 206   n  to process and/or encode the incoming audio signals AIN_A-AIN_N. In some embodiments, the processor  130  may be configured with on-chip audio processing to encode the incoming audio signals AIN_A-AIN_N. The microphones  206   a - 206   n  may capture audio of the environment  50 . The camera system  104   i  maybe configured to synchronize the audio captured with the images captured by the capture devices  202   a - 202   n.    
     The processor  130  may be configured to execute computer readable code and/or process information. The processor  130  may be configured to receive input and/or present output to the memory  132 . The processor  130  may be configured to present and/or receive other signals (not shown). The number and/or types of inputs and/or outputs of the processor  130  may be varied according to the design criteria of a particular implementation. 
     The processor  130  may receive the signals PIXELD_A-PIXELD_N, the signal DIR_AUDIO and/or the signal DATA. The processor  130  may make a decision based on data received at the inputs  220   a - 220   n,  the input  222 , the input  224   b,  the input  226  and/or other input. For example, other inputs may comprise external signals generated in response to user input, external signals generated by the microphones  206   a - 206   n  and/or internally generated signals such as signals generated by the processor  130  in response to analysis of the video frames and/or objects detected in the video frames. The processor  130  may adjust the video data (e.g., crop, digitally move, physically move the camera sensors  230   a - 230   n,  etc.) of the video frames. The processor  130  may generate the signal STAT and/or the signal DIR_AOUT in response to data received by the inputs  220   a - 220   n,  the input  222 , the input  224   b,  the input  226  and/or the decisions made in response to the data received by the inputs  220   a - 220   n,  the input  222 , the input  224   b  and/or the input  226 . 
     The signal STAT and/or the signal DIR_AOUT may be generated to provide an output in response to the captured video frames and the video analytics performed by the processor  130 . For example, the video analytics may be performed by the processor  130  in real-time and/or near real-time (e.g., with minimal delay). 
     The cropping, downscaling, blending, stabilization, packetization, encoding, compression and/or conversion performed by the processor  130  may be varied according to the design criteria of a particular implementation. For example, the video frames generated by the processor  130  may be a processed version of the signals PIXELD_A-PIXELD_N configured to enable detection of the objects  160   a - 160   n  and/or determination of the characteristics  162 . In some embodiments, the video data may be encoded at a high bitrate. For example, the signal may be generated using a lossless compression and/or with a low amount of lossiness. 
     In some embodiments, the video frames may be some view (or derivative of some view) captured by the capture devices  202   a - 202   n.  For example, the video frames may comprise a portion of the panoramic video captured by the capture devices  202   a - 202   n.  In another example, the video frames may comprise a region of interest selected and/or cropped from the panoramic video frame by the processor  130  (e.g., upscaled, oversampled and/or digitally zoomed) to enable a high precision of object detection. In some embodiments, the video frames may provide a series of cropped and/or enhanced panoramic video frames that improve upon the view from the perspective of the camera system  104   i  (e.g., provides night vision, provides High Dynamic Range (HDR) imaging, provides more viewing area, highlights detected objects, provides additional data such as a numerical distance to detected objects, etc.) to enable the processor  130  to see the location  50  better than a person would be capable of with human vision. 
     The memory  132  may store data. The memory  132  may be implemented as a cache, flash memory, DRAM memory, etc. The type and/or size of the memory  132  may be varied according to the design criteria of a particular implementation. The data stored in the memory  132  may correspond to a video file, user profiles, user permissions, the rental agreement  122 , the terms and/or entries of the rental agreement  122 , contact information for the renter/landlord, etc. 
     The lenses  200   a - 200   n  (e.g., camera lenses) may be directed to provide a panoramic view from the camera system  104   i.  The lenses  200   a - 200   n  may be aimed to capture environmental data (e.g., light). The lens  200   a - 200   n  may be configured to capture and/or focus the light for the capture devices  202   a - 202   n.  Generally, the camera sensors  230   a - 230   n  may be located behind each of the respective lenses  200   a - 200   n.  Based on the captured light from the lenses  200   a - 200   n,  the capture devices  202   a - 202   n  may generate a bitstream and/or raw pixel data. 
     Embodiments of the processor  130  may perform video stitching operations on the signals PIXELD_A-PIXELD_N. In one example, each of the pixel data signals PIXELD_A-PIXELD_N may provide a portion of a panoramic view and the processor  130  may crop, blend, synchronize and/or align the pixel data from the signals PIXELD_A-PIXELD_N to generate the panoramic video frames. In some embodiments, the processor  130  may be configured to perform electronic image stabilization (EIS). The processor  130  may perform de-warping on the video frames. The processor  130  may perform intelligent video analytics on the de-warped video frames. The processor  130  discard the video frames after the video analytics and/or computer vision has been performed. 
     The encoded video frames may be processed locally and discarded. In one example, the encoded, panoramic video may be stored locally by the memory  132  to enable the processor  130  to facilitate the computer vision analysis and then discarded. The processor  130  may discard the video frames as soon as possible after the video frames are no longer needed. Generally, after the processor  130  determines the parameters and/or statistics  170   a - 170   n,  the video frames are no longer needed. 
     The processor  130  may receive an input to generate the video frames (e.g., the signals PIXELD_A-PIXELD_N) from the CMOS sensor(s)  230   a - 230   n.  The pixel data signals PIXELD_A-PIXELD_N may be enhanced by the processor  130  (e.g., color conversion, noise filtering, auto exposure, auto white balance, auto focus, etc.). Generally, the panoramic video may comprise a large field of view generated by one or more lenses/camera sensors. One example of a panoramic video may be an equirectangular 360 video. Equirectangular 360 video may also be called spherical panoramas. Panoramic video may be a video that provides a field of view that is larger than the field of view that may be displayed on a device used to playback the video. For example, the field of view  152   a - 152   b  captured by the camera system  104   i  may be used to generate panoramic video such as a spherical video, a hemispherical video, a 360 degree video, a wide angle video, a video having less than a 360 field of view, etc. 
     Panoramic videos may comprise a view of the environment near the camera system  104   i.  In one example, the entire field of view  152   a - 152   b  of the panoramic video may be captured at generally the same time (e.g., each portion of the panoramic video represents the view from the camera system  104   i  at one particular moment in time). In some embodiments (e.g., when the camera system  104   i  implements a rolling shutter sensor), a small amount of time difference may be present between some portions of the panoramic video. Generally, each video frame of the panoramic video comprises one exposure of the sensor (or the multiple sensors  230   a - 230   n ) capturing the environment near the camera system  104   i.    
     In some embodiments, the field of view  152   a - 152   b  may provide coverage for a full 360 degree field of view. In some embodiments, less than a 360 degree view may be captured by the camera system  104   i  (e.g., a 270 degree field of view, a 180 degree field of view, etc.). In some embodiments, the panoramic video may comprise a spherical field of view (e.g., capture video above and below the camera system  104   i ). For example, the camera system  104   i  may be mounted on a ceiling and capture a spherical field of view of the area below the camera system  104   i.  In some embodiments, the panoramic video may comprise a field of view that is less than a spherical field of view (e.g., the camera system  104   i  may be configured to capture the ground below and the areas to the sides of the camera system  104   i  but nothing directly above). The implementation of the camera system  104   i  and/or the captured field of view  152   a - 152   b  may be varied according to the design criteria of a particular implementation. 
     In embodiments implementing multiple lenses, each of the lenses  200   a - 200   n  may be directed towards one particular direction to provide coverage for a full 360 degree field of view. In embodiments implementing a single wide angle lens (e.g., the lens  200   a ), the lens  200   a  may be located to provide coverage for the full 360 degree field of view (e.g., on the bottom of the camera system  104   i  in a ceiling mounted embodiment, on the bottom of a drone camera, etc.). In some embodiments, less than a 360 degree view may be captured by the lenses  200   a - 200   n  (e.g., a 270 degree field of view, a 180 degree field of view, etc.). In some embodiments, the lenses  200   a - 200   n  may move (e.g., the direction of the capture devices may be controllable). In some embodiments, one or more of the lenses  200   a - 200   n  may be configured to implement an optical zoom (e.g., the lenses  200   a - 200   n  may zoom in/out independent of each other). 
     In some embodiments, the camera system  104   i  may be implemented as a system on chip (SoC). For example, the camera system  104   i  may be implemented as a printed circuit board comprising one or more components (e.g., the capture devices  202   a - 202   n,  the processor  130 , the communication device  204 , the memory  132 , etc.). The camera system  104   i  may be configured to perform intelligent video analysis on the video frames of the de-warped, panoramic video. The camera system  104   i  may be configured to crop and/or enhance the panoramic video. 
     In some embodiments, the processor  130  may be configured to perform sensor fusion operations. The sensor fusion operations performed by the processor  130  may be configured to analyze information from multiple sources (e.g., the capture devices  202   a - 202   n  and the microphones  206   a - 206   n ). By analyzing various data from disparate sources, the sensor fusion operations may be capable of making inferences about the data that may not be possible from one of the data sources alone. For example, the sensor fusion operations implemented by the processor  130  may analyze video data (e.g., mouth movements of the subjects  70   a - 70   n ) as well as the speech patterns from the directional audio DIR_AUD. The disparate sources may be used to develop a model of a scenario to support decision making For example, the processor  130  may be configured to compare the synchronization of the detected speech patterns with the mouth movements in the video frames to determine which person in a video frame is speaking The sensor fusion operations may also provide time correlation, spatial correlation and/or reliability among the data being received. 
     In some embodiments, the processor  130  may implement convolutional neural network capabilities. The convolutional neural network capabilities may implement computer vision using deep learning techniques. The convolutional neural network capabilities may be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection. 
     The signal DIR_AOUT maybe an audio output. For example, the processor  130  may generate output audio based on information extracted from the video frames PIXELD_A-PIXELD_N. The signal DIR_AOUT may be determined based on an event and/or objects determined using the computer vision operations. In one example, the signal DIR_AOUT may comprise an audio message informing the people  70   a - 70   n  that the rental agreement  122  has been breached. In some embodiments, the signal DIR_AOUT may not be generated until an event has been detected by the processor  130  using the computer vision operations. 
     The signal DIR_AOUT may comprise directional and/or positional audio output information for the speakers  208   a - 208   n.  The speakers  208   a - 208   n  may receive the signal DIR_AOUT, process the directional and/or positional information and determine which speakers and/or which channels will play back particular audio portions of the signal DIR_AOUT. The speakers  208   a - 208   n  may generate the signals AOUT_A-AOUT_N in response to the signal DIR_AOUT. The signals AOUT_A-AOUT_N may be the audio message played to the people  70   a - 70   n.  For example, the speakers  208   a - 208   n  may emit a pre-recorded message in response to a detected event. The signal DIR_AOUT may be a signal generated in a format that provides directional information for the signals AOUT_A-AOUT_N. 
     The number of speakers  208   a - 208   n  may be varied according to the design criteria of a particular implementation. The number of speakers  208   a - 208   n  may be selected to provide sufficient directional channels for the outgoing audio (e.g., the number of speakers  208   a - 208   n  implemented may be varied based on the accuracy and/or resolution of directional audio output). In an example, 1 to 6 of the speakers  208   a - 208   n  may be implemented. In some embodiments, an audio processing component may be implemented by the speakers  208   a - 208   n  to process and/or decode the output audio signals DIR_AOUT. In some embodiments, the processor  130  may be configured with on-chip audio processing. In some embodiments, the signal DIR_AOUT may playback audio received from the remote devices  52   a - 52   n  in order to implement a 2-way real-time audio communication. 
     The video pipeline of the processor  130  may be configured to perform de-warping, cropping, enhancements, rolling shutter corrections, stabilizing, downscaling, packetizing, compression, conversion, blending, synchronizing and/or other video operations. The architecture of the video pipeline of the processor  130  may enable the video operations to be performed on high resolution video and/or high bitrate video data in real-time and/or near real-time. The video pipeline of the processor  130  may enable computer vision processing on 4K resolution video data, stereo vision processing, object detection, 3D noise reduction, fisheye lens correction (e.g., real time 360-degree dewarping and lens distortion correction), oversampling and/or high dynamic range processing. In one example, the architecture of the video pipeline may enable 4K ultra high resolution with H.264 encoding at double real time speed (e.g., 60 fps), 4K ultra high resolution with H.265/HEVC at 30 fps and/or 4K AVC encoding. Since the video frames generated by the processor  130  may be discarded after the data is extracted, encoding operations of the processor  130  may not be necessary. For example, while the processor  130  may be capable of performing the encoding operations, in the implementation of the camera systems  104   a - 104   n,  the encoding may not need to be performed. The type of video operations and/or the type of video data operated on by the processor  130  may be varied according to the design criteria of a particular implementation. 
     The sensors  230   a - 230   n  may each implement a high-resolution sensor. Using the high resolution sensors  230   a - 230   n,  the processor  130  may combine over-sampling of the image sensors  230   a - 230   n  with digital zooming within a cropped area. The over-sampling and digital zooming may each be one of the video operations performed by the processor  130 . The over-sampling and digital zooming may be implemented to deliver higher resolution images within the total size constraints of a cropped area. 
     In some embodiments, one or more of the lenses  200   a - 200   n  may implement a fisheye lens. One of the video operations implemented by the processor  130  may be a dewarping operation. The processor  130  may be configured to dewarp the video frames generated. The dewarping may be configured to reduce and/or remove acute distortion caused by the fisheye lens and/or other lens characteristics. For example, the dewarping may reduce and/or eliminate a bulging effect to provide a rectilinear image. 
     The processor  130  may be configured to crop (e.g., trim to) a region of interest from a full video frame (e.g., generate the region of interest video frames). The processor  130  may generate the video frames and select an area. In an example, cropping the region of interest may generate a second image. The cropped image (e.g., the region of interest video frame) may be smaller than the original video frame (e.g., the cropped image may be a portion of the captured video). 
     The area of interest may be dynamically adjusted based on the location of an audio source. For example, the detected audio source may be moving, and the location of the detected audio source may move as the video frames are captured. The processor  130  may update the selected region of interest coordinates and dynamically update the cropped section (e.g., the directional microphones  106   a - 106   n  may dynamically update the location based on the directional audio captured). The cropped section may correspond to the area of interest selected. As the area of interest changes, the cropped portion may change. For example, the selected coordinates for the area of interest may change from frame to frame, and the processor  130  may be configured to crop the selected region in each frame. 
     The processor  130  may be configured to over-sample the image sensors  230   a - 230   n.  The over-sampling of the image sensors  230   a - 230   n  may result in a higher resolution image. The processor  130  may be configured to digitally zoom into an area of a video frame. For example, the processor  130  may digitally zoom into the cropped area of interest. For example, the processor  130  may establish the area of interest based on the directional audio, crop the area of interest, and then digitally zoom into the cropped region of interest video frame. 
     The dewarping operations performed by the processor  130  may adjust the visual content of the video data. The adjustments performed by the processor  130  may cause the visual content to appear natural (e.g., appear as seen by a person viewing the location corresponding to the field of view of the capture devices  202   a - 202   n ). In an example, the dewarping may alter the video data to generate a rectilinear video frame (e.g., correct artifacts caused by the lens characteristics of the lenses  200   a - 200   n ). The dewarping operations may be implemented to correct the distortion caused by the lenses  200   a - 200   n.  The adjusted visual content may be generated to enable more accurate and/or reliable object detection. 
     Various features (e.g., dewarping, digitally zooming, cropping, etc.) may be implemented in the processor  130  as hardware modules. Implementing hardware modules may increase the video processing speed of the processor  130  (e.g., faster than a software implementation). The hardware implementation may enable the video to be processed while reducing an amount of delay. The hardware components used maybe varied according to the design criteria of a particular implementation. 
     The processor  130  is shown comprising a number of blocks (or circuits)  240   a - 240   n.  The blocks  240   a - 240   n  may implement various hardware modules implemented by the processor  130 . The hardware modules  240   a - 240   n  may be configured to provide various hardware components to implement a video processing pipeline. The circuits  240   a - 240   n  may be configured to receive the pixel data PIXELD_A-PIXELD_N, generate the video frames from the pixel data, perform various operations on the video frames (e.g., de-warping, rolling shutter correction, cropping, upscaling, image stabilization, etc.), prepare the video frames for communication to external hardware (e.g., encoding, packetizing, color correcting, etc.), parse feature sets, implement various operations for computer vision, etc. Various implementations of the processor  130  may not necessarily utilize all the features of the hardware modules  240   a - 240   n.  The features and/or functionality of the hardware modules  240   a - 240   n  may be varied according to the design criteria of a particular implementation. Details of the hardware modules  240   a - 240   n  may be described in association with U.S. patent application Ser. No. 16/831,549, filed on Apr. 16, 2020, U.S. patent application Ser. No. 16/288,922, filed on Feb. 28, 2019 and U.S. patent application Ser. No. 15/593,493 (now U.S. Pat. No. 10,437,600), filed on May 12, 2017, appropriate portions of which are hereby incorporated by reference in their entirety. 
     The hardware modules  240   a - 240   n  may be implemented as dedicated hardware modules. Implementing various functionality of the processor  130  using the dedicated hardware modules  240   a - 240   n  may enable the processor  130  to be highly optimized and/or customized to limit power consumption, reduce heat generation and/or increase processing speed compared to software implementations. The hardware modules  240   a - 240   n  may be customizable and/or programmable to implement multiple types of operations. Implementing the dedicated hardware modules  240   a - 240   n  may enable the hardware used to perform each type of calculation to be optimized for speed and/or efficiency. For example, the hardware modules  240   a - 240   n  may implement a number of relatively simple operations that are used frequently in computer vision operations that, together, may enable the computer vision algorithm to be performed in real-time. The video pipeline may be configured to recognize objects. Objects may be recognized by interpreting numerical and/or symbolic information to determine that the visual data represents a particular type of object and/or feature. For example, the number of pixels and/or the colors of the pixels of the video data may be used to recognize portions of the video data as objects. 
     One of the hardware modules  240   a - 240   n  (e.g.,  240   a ) may implement a scheduler circuit. The scheduler circuit  240   a  may be configured to store a directed acyclic graph (DAG). In an example, the scheduler circuit  240   a  may be configured to generate and store the directed acyclic graph in response to the feature set information received in the signal FEAT_SET. The directed acyclic graph may define the video operations to perform for extracting the data  170   a - 170   n  from the video frames. For example, the directed acyclic graph may define various mathematical weighting to apply when performing computer vision operations to classify various groups of pixels as particular objects. 
     The scheduler circuit  240   a  may be configured to parse the acyclic graph to generate various operators. The operators may be scheduled by the scheduler circuit  240   a  in one or more of the other hardware modules  240   a - 240   n.  For example, one or more of the hardware modules  240   a - 240   n  may implement hardware engines configured to perform specific tasks (e.g., hardware engines designed to perform particular mathematical operations that are repeatedly used to perform computer vision operations). The scheduler circuit  240   a  may schedule the operators based on when the operators may be ready to be processed by the hardware engines  240   a - 240   n.    
     The scheduler circuit  240   a  may time multiplex the tasks to the hardware modules  240   a - 240   n  based on the availability of the hardware modules  240   a - 240   n  to perform the work. The scheduler circuit  240   a  may parse the directed acyclic graph into one or more data flows. Each data flow may include one or more operators. Once the directed acyclic graph is parsed, the scheduler circuit  240   a  may allocate the data flows/operators to the hardware engines  240   a - 240   n  and send the relevant operator configuration information to start the operators. 
     Each directed acyclic graph binary representation may be an ordered traversal of a directed acyclic graph with descriptors and operators interleaved based on data dependencies. The descriptors generally provide registers that link data buffers to specific operands in dependent operators. In various embodiments, an operator may not appear in the directed acyclic graph representation until all dependent descriptors are declared for the operands. 
     One of the hardware modules  240   a - 240   n  (e.g.,  240   b ) may implement a convolutional neural network (CNN) module. The CNN module  240   b  may be configured to perform the computer vision operations on the video frames. The CNN module  240   b  may be configured to implement recognition of the objects  160   a - 160   n  through multiple layers of feature detection. The CNN module  240   b  may be configured to calculate descriptors based on the feature detection performed. The descriptors may enable the processor  130  to determine a likelihood that pixels of the video frames correspond to particular objects (e.g., the people  70   a - 70   n,  pets, items, etc.). 
     The CNN module  240   b  may be configured to implement convolutional neural network capabilities. The CNN module  240   b  may be configured to implement computer vision using deep learning techniques. The CNN module  240   b  may be configured to implement pattern and/or image recognition using a training process through multiple layers of feature-detection. The CNN module  240   b  may be configured to conduct inferences against a machine learning model. 
     The CNN module  240   b  may be configured to perform feature extraction and/or matching solely in hardware. Feature points typically represent interesting areas in the video frames (e.g., corners, edges, etc.). By tracking the feature points temporally, an estimate of ego-motion of the capturing platform or a motion model of observed objects in the scene may be generated. In order to track the feature points, a matching algorithm is generally incorporated by hardware in the CNN module  240   b  to find the most probable correspondences between feature points in a reference video frame and a target video frame. In a process to match pairs of reference and target feature points, each feature point may be represented by a descriptor (e.g., image patch, SIFT, BRIEF, ORB, FREAK, etc.). Implementing the CNN module  240   b  using dedicated hardware circuitry may enable calculating descriptor matching distances in real time. 
     The CNN module  240   b  may be a dedicated hardware module configured to perform feature detection of the video frames. The features detected by the CNN module  240   b  may be used to calculate descriptors. The CNN module  240   b  may determine a likelihood that pixels in the video frames belong to a particular object and/or objects in response to the descriptors. For example, using the descriptors, the CNN module  240   b  may determine a likelihood that pixels correspond to a particular object (e.g., a person, an item of furniture, a picture of a person, a pet, etc.) and/or characteristics of the object (e.g., a mouth of a person, a hand of a person, a screen of a television set, an armrest of a couch, a clock, etc.). Implementing the CNN module  240   b  as a dedicated hardware module of the processor  130  may enable the camera system  104   i  to perform the computer vision operations locally (e.g., on-chip) without relying on processing capabilities of a remote device (e.g., communicating data to a cloud computing service). 
     The computer vision operations performed by the CNN module  240   b  may be configured to perform the feature detection on the video frames in order to generate the descriptors. The CNN module  240   b  may perform the object detection to determine regions of the video frame that have a high likelihood of matching the particular object. In one example, the types of object to match against (e.g., reference objects) may be customized using an open operand stack (enabling programmability of the processor  130  to implement various directed acyclic graphs each providing instructions for performing various types of object detection). The CNN module  240   b  may be configured to perform local masking to the region with the high likelihood of matching the particular object(s) to detect the object. 
     In some embodiments, the CNN module  240   b  may determine the position (e.g., 3D coordinates and/or location coordinates) of various features (e.g., the characteristics  162 ) of the detected objects  160   a - 160   n.  In one example, the location of the arms, legs, chest and/or eyes may be determined using 3D coordinates. One location coordinate on a first axis for a vertical location of the body part in 3D space and another coordinate on a second axis for a horizontal location of the body part in 3D space may be stored. In some embodiments, the distance from the lenses  200   a - 200   n  may represent one coordinate (e.g., a location coordinate on a third axis) for a depth location of the body part in 3D space. Using the location of various body parts in 3D space, the processor  130  may determine body position, and/or body characteristics of the people  70   a - 70   n.    
     The CNN module  240   b  may be pre-trained (e.g., configured to perform computer vision to detect objects based on the training data received to train the CNN module  240   b ). For example, the results of training data (e.g., a machine learning model) maybe pre-programmed and/or loaded into the processor  130 . The CNN module  240   b  may conduct inferences against the machine learning model (e.g., to perform object detection). The training may comprise determining weight values for each of the layers. For example, weight values may be determined for each of the layers for feature extraction (e.g., a convolutional layer) and/or for classification (e.g., a fully connected layer). The weight values learned by the CNN module  240   b  may be varied according to the design criteria of a particular implementation. 
     The convolution operation may comprise sliding a feature detection window along the layers while performing calculations (e.g., matrix operations). The feature detection window may apply a filter to pixels and/or extract features associated with each layer. The feature detection window may be applied to a pixel and a number of surrounding pixels. In an example, the layers may be represented as a matrix of values representing pixels and/or features of one of the layers and the filter applied by the feature detection window may be represented as a matrix. The convolution operation may apply a matrix multiplication between the region of the current layer covered by the feature detection window. The convolution operation may slide the feature detection window along regions of the layers to generate a result representing each region. The size of the region, the type of operations applied by the filters and/or the number of layers may be varied according to the design criteria of a particular implementation. 
     Using the convolution operations, the CNN module  240   b  may compute multiple features for pixels of an input image in each extraction step. For example, each of the layers may receive inputs from a set of features located in a small neighborhood (e.g., region) of the previous layer (e.g., a local receptive field). The convolution operations may extract elementary visual features (e.g., such as oriented edges, end-points, corners, etc.), which are then combined by higher layers. Since the feature extraction window operates on a pixel and nearby pixels, the results of the operation may have location invariance. The layers may comprise convolution layers, pooling layers, non-linear layers and/or fully connected layers. In an example, the convolution operations may learn to detect edges from raw pixels (e.g., a first layer), then use the feature from the previous layer (e.g., the detected edges) to detect shapes in a next layer and then use the shapes to detect higher-level features (e.g., facial features, pets, furniture, etc.) in higher layers and the last layer may be a classifier that uses the higher level features. 
     The CNN module  240   b  may execute a data flow directed to feature extraction and matching, including two-stage detection, a warping operator, component operators that manipulate lists of components (e.g., components may be regions of a vector that share a common attribute and may be grouped together with a bounding box), a matrix inversion operator, a dot product operator, a convolution operator, conditional operators (e.g., multiplex and demultiplex), a remapping operator, a minimum-maximum-reduction operator, a pooling operator, a non-minimum, non-maximum suppression operator, a scanning-window based non-maximum suppression operator, a gather operator, a scatter operator, a statistics operator, a classifier operator, an integral image operator, comparison operators, indexing operators, a pattern matching operator, a feature extraction operator, a feature detection operator, a two-stage object detection operator, a score generating operator, a block reduction operator, and an upsample operator. The types of operations performed by the CNN module  240   b  to extract features from the training data may be varied according to the design criteria of a particular implementation. 
     Each of the hardware modules  240   a - 240   n  may implement a processing resource (or hardware resource or hardware engine). The hardware engines  240   a - 240   n  may be operational to perform specific processing tasks. In some configurations, the hardware engines  240   a - 240   n  may operate in parallel and independent of each other. In other configurations, the hardware engines  240   a - 240   n  may operate collectively among each other to perform allocated tasks. One or more of the hardware engines  240   a - 240   n  may be homogenous processing resources (all circuits  240   a - 240   n  may have the same capabilities) or heterogeneous processing resources (two or more circuits  240   a - 240   n  may have different capabilities). 
     Referring to  FIG.  5   , a diagram illustrating detecting a breach of a rental property agreement is shown. An example scenario  250  is shown. The example scenario  250  may comprise the server  102 , remote devices  52   b - 52   n  and/or the camera system  104 . The capture device  202   a  and the microphone  206   a  are shown on the camera system  104 . The processor  130  is shown within the camera system  104 . The example scenario  250  may further comprise a visualization of the memory  132 , the processor  130  and/or a rental agreement  122 . 
     The server  102  may be configured to generate the rental agreement  122 . The rental agreement  122  may comprise a text (e.g., human-readable) version  252  and a machine readable version  254 . The text version  252  of the rental agreement  122  may be used to enable both the property owner and the renter to view and understand the terms and conditions of the rental agreement  122 . The machine readable version  254  may comprise a neural network and/or computer readable instructions that define the terms and conditions of the rental agreement  122  that may be readable by the processor  130  of the camera system  104  in order to generate the detection parameters). For example, the machine readable version  254  may be generated according to an API (e.g., a format) compatible with the camera system  104 . The machine readable version  254  of the rental agreement  122  may comprise one or more neural networks (e.g, each neural network may correspond to various types of objects to detect based on the terms and conditions of the rental agreement  122 ). The text version  252  and/or the machine readable version  254  of the rental agreement may be provided to the camera system  104  in the signal FEAT_SET. 
     The text version  252  and/or the machine readable version  254  of the rental agreement  122  may define what may be considered a breach of the rental agreement  122  at the location  50 . The renter and the landlord may refer to the text version  252  of the rental agreement  122  to determine what is acceptable usage of the location  50  and what may not be acceptable usage of the location  50 . The camera system  104  may analyze the video frames based on the machine readable version  254 . The machine readable version  254  may correspond to the text version  252 . In an example, if one of the terms of the rental agreement is that only four people are allowed at the location  50 , then the text version  252  may comprise an entry (e.g., “Number of guests allowed: 4”) and the machine readable version  254  may comprise instructions for the processor  130  to search the video frames to determine the number of people  70   a - 70   n  detected and determine whether more than four guests have been detected. The type of data and/or the format of the text version  252  and/or the machine readable version  254  may be varied according to the design criteria of a particular implementation. 
     The camera system  104  is shown receiving the signal FEAT_SET based on the rental agreement  122  from the server  102 . The camera system  104  is shown generating the signals NTF_A-NTF_N. In the example shown, the signal NTF_A may be communicated to the server  102  and the signals NTF_B-NTF_N may be communicated to the remote devices  52   b - 52   n.  In some embodiments, the camera system  104  may communicate the signal STAT (as shown in association with  FIG.  4   ) to the server  102  and the server  102  may communicate the signal(s) NTF_A-NTF_N to the remote devices  52   a - 52   n.  In an example, the server  102  may securely store the contact information for the owners of the smartphones  52   a - 52   n  (e.g., the renters and/or the landlords). 
     The camera system  104  may receive the signal FEAT_SET from the server  102 . The processor  130  may convert the feature set information in the signal FEAT_SET to detection parameters. The camera system  104  may capture pixel data of the rental property location  50  and generate the video frames from the pixel data PIXELD_A-PIXELD_N. The camera system  104  is shown generating a signal (e.g., VIDEO_DATA). The signal VIDEO_DATA may comprise the video frames generated by the processor  130 . The signal VIDEO_DATA may comprise data used internally by the processor  130 . In an example, the signal VIDEO_DATA may never be communicated from the camera system  104 . 
     In the example scenario  250 , the video frames VIDEO_DATA are shown being used by the processor  130 . In an example, the video frames may be operated on using a video processing pipeline implemented by processor  130  of the camera system  104 . The processor  130  may comprise a block (or circuit)  260  and/or a block (or circuit)  262 . The block  260  may implement computer vision modules. The block  262  may represent a discard location of the processor  130 . The computer vision modules  260  and/or the discard location  262  may comprise components of the video processing pipeline of the processor  130 . The processor  130  may comprise other components (not shown). The number, type and/or arrangement of the components of the processor  130  may be varied according to the design criteria of a particular implementation. 
     Video frames  270   a - 270   n  are shown being operated on by the computer vision modules  260 . The video frames  270   a - 270   n  may be the data in the signal VIDEO_FRAMES. The video frames  270   a - 270   n  may be generated by the processor  130  in response to the pixel data PIXELD_A-PIXELD_N received from the capture devices  202   a - 202   n.  In the example shown, the video frame  270   a  may generally correspond to the location  50  shown in association with  FIG.  2   . The computer vision modules  260  of the processor  130  may perform the video operations (e.g., the analytics using computer vision) locally (e.g., the video frames  270   a - 270   n  may not leave the apparatus  104 ). 
     The feature set information in the signal FEAT_SET may comprise instructions that may be compatible with the computer vision modules  260  of the processor  130 . In an example, the signal FEAT_SET may be configured to provide the machine readable version  254  of the rental agreement  122  in a format that may be compatible with the camera system  104 . The feature set information may provide the parameters that the computer vision modules  260  may use to analyze the video frames  270   a - 270   n.  The parameters may define the criteria that the computer vision modules  260  use to determine whether there has been a breach of the rental agreement  122 . The signal FEAT_SET may comprise parameters for video (e.g., pet detection, types of pets allowed, behavior allowed, number of guests allowed, locations of particular items, etc.). The signal FEAT_SET may further comprise parameters for audio (e.g., a maximum audio level, frequencies allowed, times of day that particular audio levels are allowed, etc.). Generally, the computer vision modules  260  may compare the characteristics  162  detected in the video frames  270   a - 270   n  with the rental agreement  122  by using the parameters defined by the feature set information. The information provided in the feature set information may be varied according to the design criteria of a particular implementation. 
     Part of the video processing pipeline of the processor  130  may comprise the computer vision modules  260 . The computer vision modules  260  may be configured to perform object detection, classify objects, and/or extract data from the video frames  270   a - 270   n.  The computer vision modules  260  may be configured to detect the objects  160   a - 160   n  in the video frames  270   a - 270   n  and/or generate the extracted data  170   a - 170   n  about the detected objects  160   a - 160   n  based on the characteristics  162  of the objects  160   a - 160   n.  In the example diagram shown, the video frames  270   a - 270   n  (e.g., the signal VIDEO_DATA) may be presented to computer vision modules  260  of the video processing pipeline. The processor  130  may compare the data extracted with the detection parameters (e.g., the feature set information) of the signal FEAT_SET according to the machine readable version  254  of the rental agreement  122  to determine whether or not there has been a breach of the conditions of the rental agreement  122 . 
     After the video frames  270   a - 270   n  have been analyzed by the computer vision operations performed by the processor  130 , the video frames  270   a - 270   n  may be discarded. In the example shown, the garbage can  262  may represent the processor  130  implementing a discarding method for the camera system  104 . The video frames  270   a - 270   n  may be passed from the video processing pipeline to the deletion function  262  to make the video frames  270   a - 270   n  unavailable (e.g., flushed from cache, flagged to be overwritten, redirected to /dev/null, etc.). To ensure that privacy of the renter (or other people) is protected, no images or audio may ever leave the camera system  104 . The video data and/or audio data may be eliminated after processing and may be unavailable for communication beyond the camera system  104  (e.g., not be stored to long-term memory, not communicated to another device, etc.). 
     In the example shown, the video processing pipeline of the processor  130  may detect a breach in the terms of the rental agreement  122  (e.g., too many people have been detected at the rental property  50 ). For example, the feature set may provide instructions for counting the number of people in the video frames  270   a - 270   n,  and the computer vision modules  260  may detect a greater number of visitors (e.g., 5) than the maximum allowable number of visitors in the rental agreement  122  (e.g., 3). The computer vision modules  260  may extract the data  170  that indicates the number of people in the video frames  270   a - 270   n  (and additional data according to the other detection parameters). In the example shown, the extracted data  170  may indicate a breach of the rental agreement  122 . 
     Data that does not correspond to the detection parameters may be ignored. In one example, if pets are not part of the rental agreement  122  (e.g., the rental agreement does not restrict having pets at the location  50 ), then the computer vision modules  260  may not search for pets in video frames  270   a - 270   n.  In another example, if pets are not part of the rental agreement  122 , the computer vision modules  260  may still perform a search for pets as part of the general computer vision operations performed, but the information about the pets may not be included as part of the extracted data  170  (e.g., any information not defined by the rental agreement may be discarded along with the video frames  270   a - 270   n ). 
     The extracted data  170  may be stored by the camera system  104 , but the video frames and/or the audio that corresponds to the data extracted may be deleted. The extracted data  170  is shown stored as the parameters and statistics in the memory  132 . The extracted data  170  (e.g., the parameters and statistics) may be used by the processor  130  to determine whether there has been a breach of the rental agreement  122 . When the computer vision modules  260  detect that the extracted data  170  matches the detection parameters of the machine readable version  254 , the breach of the rental agreement  122  may be detected. In an example, the feature set signal FEAT_SET may provide instructions to detect whether there are more than three visitors at the rental property  50  and the camera system  104  may use the detection parameters to monitor whether more than three visitors have been detected in the video frames  270   a - 270   n.  In some embodiments, the computer vision modules  260  and/or audio analytics modules may determine when the breach of the rental agreement  122  has been detected. In some embodiments, the processor  130  may analyze the parameters and statistics  170  stored in the memory  132  after the video frames  270   a - 270   n  have been discarded to determine whether a breach of the rental agreement  122  has been detected. 
     In response to the breach of the rental agreement  122 , the camera system  104  may generate a notification. In the example shown, multiple notifications NTF_A-NTF_N may be generated. In some embodiments, the camera system  104  may not receive contact information about the renter or the property owner. The camera system  104  may provide a notification signal (e.g., NTF_A) to the server  102 , and then the server  102  may contact the property owner and/or the renter (e.g., the server  102  may store the contact information of the users). In some embodiments, the signal FEAT_SET may comprise the contact information of the users and the camera system  104  may use the communications device  204  to contact the property owner and/or the renter. For example, a signal (e.g., NTF_B) may be communicated to the smartphone (or other computing device)  52   b  of the property owner and a signal (e.g., NTF_N) may be communicated to the smartphones (or other computer devices)  52   n  of the renter. The camera system  104  may be configured to provide the notification to other parties as defined in the rental agreement  122 . The notification may indicate that there has been a breach of the terms of the rental agreement  122 . The notification may not comprise the video and/or audio associated with the breach of the rental agreement (e.g., the video and/or audio may no longer be available). 
     In some embodiments, the camera system  104  may provide the signal STAT to the server  102 . The signal STAT may comprise the extracted data  170 . The server  102  may use the extracted data  170  (e.g., the parameters and statistics) to determine whether there has been a breach of the rental agreement  122  and/or generate the notification signals NTF_A-NTF_N. 
     Referring to  FIG.  6   , a diagram illustrating an example notification for a breach of a rental agreement is shown. An example scenario  400  is shown. The example scenario  400  may comprise a hand  70   i  holding the smartphone  52   i.  The hand  70   i  may be a hand of the renter or a hand of the property owner. The smartphone  52   i  is shown displaying the companion app  302 . The companion app  302  may be configured to display the notification of a breach of the terms of the rental agreement  122 . 
     The notification shown by the companion app  302  may be generated in response to one of the notification signals NTF_A-NTF_N. In the example shown, the notification may be displayed by the companion app  302 . However, the notification may be communicated by other means. In one example, the notification may be provided by a text message (e.g., SMS). In another example, the notification may be provided by a phone call (e.g., an automated voice message). In yet another example, the notification may be provided via email. The format of the notification may be varied according to the design criteria of a particular implementation. 
     An example notification is shown displayed on the smartphone  52   i.  The notification may comprise a message  310 . The message  310  may indicate that a breach of the rental agreement  122  has been detected. Additional information about the breach of the rental agreement  122  may be displayed. A property address  312  is shown as part of the notification. The property address  312  may indicate the address of the rental property that corresponds to the detected breach. In an example, the property owner may rent out multiple properties and the property address  312  may provide clarity about which property has the detected breach of the rental property agreement  122 . 
     In the example shown, the breach to the rental agreement  122  may be the number of guests detected by the camera system  104 . The notification may provide details  314 - 318  about the breach of the rental agreement  122 . The details  314 - 318  may comprise the detection  314 , the condition  316  and/or a timestamp  318  of the breach detected based on the statistics and parameters  170   a - 170   n  detected by the processor  130 . 
     For the example of a breach detected for the maximum number of allowable guests, the detection  314  may indicate the number of guests detected by the camera system  104 . In the example shown (as shown in association with  FIG.  2   ), the number of detected guests  314  may be five. The corresponding condition  316  may be the maximum number of allowed guests according to the rental agreement  122 . In the example shown, the maximum number of guests  316  according to the rental agreement  122  may be three. The timestamp  318  may comprise the time that the breach was detected. In the example shown, the detection  314  of five guests may have been detected by the camera system  104  at three in the morning. Other information may be provided based on the extracted data  170   a - 170   n  that corresponds to the breach of the rental agreement  122 . For example, if a different breach of the agreement  122  was detected (e.g., audio level), the details  314 - 318  may comprise alternate data types (e.g., maximum sound level of the rental agreement  122 , the sound level detected, etc.). 
     A button  320 , a button  322  and a button  324  are shown as part of the example notification. The button  320  may enable the end user to access the text version  252  of the rental agreement  122 . For example, the end user may want to review the terms of the rental agreement  122 . 
     The button  322  may enable the end user to contact the other party in the rental agreement  122 . The contact other party button  322  may enable the property owner and the renter to communicate. For example, if the notification is provided to the property owner, then the property owner may contact the renter to inform the renter that the extra guests should leave and if they do not leave then the authorities will be contacted. In another example, if the notification is provided to the renter, then the renter may contact the property owner to inform them that the extra guests have left. The contact other party button  322  may be implemented to enable the parties to remedy the detected breach. 
     The button  324  may enable the users to view a data transparency log. The data transparency log may provide access to all the data stored by the servers  102   a - 102   n  and/or the camera systems  104   a - 104   n  about the users. The users may access the data transparency log to ensure the accuracy of any data collected about the users. In one example, the data transparency log may record the number of people that were detected at the location  50  and at what time. The data transparency log may be used to help resolve a dispute between the landlord and the renter. In an example, the data transparency log may be referenced as feedback (e.g., whether the detections by the camera systems  104   a - 104   n  are detecting the terms of the rental agreement  122  accurately) and/or as proof of a violation. In another example, the users may access the data transparency log to verify what the camera systems  104   a - 104   n  have extracted (e.g., view the extracted data  170   a - 170   n ) to see data corresponding to the entries  370   a - 370   n  may be stored (e.g., the number of guests was recorded), but there were no identifiers of a particular person and no video/audio stored. 
     The notification may provide a privacy statement  330 . The privacy statement  330  may inform the recipient of the notification that the video (e.g., the video frames  270   a - 270   n ) and/or audio (e.g., the signals AIN_A-AIN_N) is not available because the video and/or audio has never been stored. Since the video frames  270   a - 270   n  and/or audio captured may be discarded after the processor  130  analyzes the video and/or audio, the video frames  270   a - 270   n  and/or the captured audio may be unavailable for communication. The privacy statement  330  may further indicate that the determination of the breach of the rental agreement  122  may be based on the extracted statistics and parameters  170   a - 170   n.    
     Referring to  FIG.  7   , a diagram illustrating an example of comparing statistics and parameters extracted from video and/or audio data to entries of a rental agreement is shown. An example comparison  350  of the rental agreement  122  and the detected parameters and statistics  170  are shown. 
     The CV modules  260  are shown extracting data from the video frames  270   a - 270   n.  The CV modules  260  may extract the data to generate the parameters and statistics  170 . As the parameters and statistics  170  are generated from the video frames  270   a - 270   n,  the video frames  270   a - 270   n  may be discarded. In an example, after the processor  130  extracts the data from the video frame  270   a,  the video frame  270   a  may be discarded. One or more of the video frames  270   a - 270   n  may be processed in parallel and/or may be operated on at different sections of the video processing pipeline. Once all the parameters and statistics  170  are generated from one video frame, that video frame may be discarded. The other video frames  270   a - 270   n  may continue to be passed through the video processing pipeline of the processor  130 . 
     Examples of the parameters and statistics  170  are shown. The parameters and statistics  170  may comprise a timestamp  352 . The timestamp  352  may indicate the time that the video frame  270   a - 270   n  corresponding to the extracted data  170  was recorded. In an example, the timestamp  352  maybe used to generate the time of breach notification  318  shown in association with  FIG.  6   . In some embodiments, some of the entries of the rental agreement  122  may be time sensitive (e.g., before 10 pm ten guests may be allowed, but after 10 pm only 3 guests may be allowed). The timestamp  352  may provide a temporal reference for when the parameters  170  were extracted. 
     The parameters  170  may comprise detections  354   a - 354   n.  The detections  354   a - 354   n  may be the information that may be determined based on the parameters and statistics  170 . Generally, the detections  354   a - 354   n  may correspond to the entries of the rental agreement  122 . For example, in order to protect the privacy of the renters, the amount of data collected may be limited. For example, if the rental agreement  122  does not provide a limitation on the number of guests, then the detections  354   a - 354   n  may not comprise information about the number of guests. 
     The detection  354   a  may be the number of people detected. In the example shown, the number of people detected may be five. The detection  354   b  may be the number of pets detected. In the example shown, no pets may be detected. If a pet was detected, further detections  354   a - 354   n  may be extracted (e.g., the type of pet, where the pet was kept on the property, etc.). The detection  354   c  may be the audio level detected. In the example shown, the detected audio level may be 80 dB. The detection  354   d  may be an item condition. In the example shown, the condition of the item (e.g., a TV) may be good (e.g., undamaged, not moved, not used, etc.). The detections  354   a - 354   n  may comprise data extracted from both the video frames  270   a - 270   n  and the captured audio AIN_A-AIN_N. The detections  354   a - 354   n  made by the camera system  104  may be performed in response to the machine readable version  254  of the rental agreement  122 . The types of detections extracted in the parameters and statistics  170  may be varied according to the design criteria of a particular implementation. 
     The rental agreement  122  is shown. For illustrative purposes, the text version  252  of the rental agreement  122  is shown. The rental agreement  122  may comprise a property address  360 . In an example, the property address  360  may correspond to the property address  312  shown in association with the notification shown in  FIG.  6   . 
     The address  360  may define the location  50 . The rental agreement  122  may comprise a time period  362   a - 362   b.  The time period  362   a - 362   b  may comprise a rental start time  362   a  and a rental end time  362   b.  The rental time period  362   a - 362   b  may define the time that the rental agreement  122  is active. The rental time period  362   a - 362   b  may indicate at which times the camera system  104  may use the feature set defined in the machine readable version  254  of the rental agreement  122 . For example, when the current time is outside of the time period  362   a - 362   b,  then the camera system  104  may not use the machine readable version  254  of the rental agreement  122  to extract the data from the video frames  270   a - 270   n.    
     The rental agreement  122  may further define a number of cameras  364  and/or a location of cameras  366   a - 366   c.  The number of cameras  364  may indicate how many of the camera systems  104   a - 104   n  are implemented at the rental property  50 . The location of cameras  366   a - 366   c  may define where the camera systems  104   a - 104   n  are located. Defining the number of cameras  364  and/or the location of cameras  366   a - 366   c  may provide transparency for the renters (e.g., to inform the renters that cameras are detecting behavior and/or watching for breaches to the rental agreement  122 ). Defining the number of cameras  364  and/or the location of cameras  366   a - 366   c  may further ensure that the renters know where the camera systems  104   a - 104   n  are located to prevent accidental damage and/or accidentally obscuring the camera systems  104   a - 104   n.    
     Entries  370   a - 370   e  are shown. The entries  370   a - 370   e  may define the terms of the rental agreement  122 . The entries  370   a - 370   e  may define the machine readable instructions  254  to be used by the camera systems  104   a - 104   n.  In some embodiments, the entries  370   a - 370   e  may be pre-defined types of detections that may be performed by the camera systems  104   a - 104   n.  The renter and the property owner may agree on the criteria for the pre-defined types of detections. While five entries  370   a - 370   e  are shown in the example  350 . Any number of entries  370   a - 370   n  may be defined in the rental agreement  122 . In some embodiments, the entries  370   a - 370   n  available and/or criteria limitations for the entries  370   a - 370   n  may be limited by the detection capabilities of the camera systems  104   a - 104   n.  The number and/or types of the entries  370   a - 370   n  may be varied according to the design criteria of a particular implementation. 
     The entry  370   a  may be the number of guests allowed on the rental property  50 . In the example shown, the number of guests allowed  370   a  may be four. The entry  370   b  may be the number of visitors allowed. In the example shown, the number of guests allowed  370   b  may be three. For example, the number of guests allowed  370   a  may define how many people may be allowed at any time, while the number of visitors  370   b  may define how many additional people may be allowed at particular times (e.g., visitors may be allowed before midnight). 
     The entry  370   c  may be whether pets are allowed. In the example shown, the pets entry  370   c  may be that pets are allowed. The entry  370   d  may be a maximum audio level. For example, the maximum audio level may be defined as described in association with U.S. patent application Ser. No. 16/858,230, filed on Apr. 24, 2020, appropriate portions of which are hereby incorporated by reference. In the example shown, the maximum audio level  370   d  maybe 75 dB. The entry  370   e  may be whether pool access is allowed. In some embodiments, the property owner may not want renters using certain areas of the rental property (e.g., not allowed to use the pool, not allowed in a particular bedroom, etc.). In the example shown, the pool access entry  370   e  may be that pool access is not allowed. 
     The processor  130  and/or the server  102  may compare the entries  370   a - 370   n  of the rental agreement  122  to the detections  354   a - 354   n  extracted from the video data and/or audio data by the camera system  104 . The comparison may determine whether there has been a breach of the terms of the rental agreement  122 . 
     In the example shown, the people detected  354   a  may be five. Comparing to the number of guests entry  370   a  (e.g., four), more than four guests have been detected. However, the timestamp  352  maybe before midnight. Since the visitor entry  370   b  provides for three guests before midnight, then the people detected  354   a  may not breach the rental agreement  122 . 
     In the example shown, the pets detected  354   b  may be zero. Since the pets entry  370   c  of the rental agreement  122  allows for pets, the pets detected  354   b  may not breach the rental agreement. The detected audio level  354   c  maybe 80 dB. Since the maximum audio level entry  370   d  may be 75 dB, the audio level detected  354   c  may breach the terms of the rental agreement. The server  102  and/or the camera system  104  may generate the signal NTF to provide a notification to the renter and/or the property owner that the maximum audio level entry  370   d  of the rental agreement  122  has been breached. 
     Referring to  FIG.  8   , a diagram illustrating an example interface for a rental offer is shown. An example scenario  400  is shown. The example scenario  400  may comprise a view of a web browser  80 . The web browser  80  may comprise various interface features (e.g., tabs, address bar, navigation buttons, etc.). A tab  82  of the web browser  80  is shown. The tab  82  may comprise a website title indicating an Owner Listing. A URL  84  is shown in the web browser  80 . In an example, the URL  84  may be a web address that points to the servers  102   a - 102   n.  The browser  80  is shown displaying a web page. The web page displayed by the browser  80  may be the web interface  126 . In an example, the property owner user device  52   a  may access the servers  102   a - 102   n  using the web browser  80 . The servers  102   a - 102   n  may generate the signal WEB to present the web interface  126  to the property owner user device  52   a.    
     The web interface  126  generated by the servers  102   a - 102   n  shown may be a property listing form interface  402 . For example, the data from the web interface  126  stored by the servers  102   a - 102   n  may be used to provide data that the browser  80  may use to output the property listing form interface  402 . The property listing form interface  402  may be an example website shown to the property owner. The property listing form interface  402  may enable the property owner to list the property  50  for rent. 
     The property listing form interface  402  may provide various fields to accept input from the property owner. The fields may correspond to terms and/or conditions that may be used to create the entries  370   a - 370   n  of the rental agreement  122 . The fields may be generated in response to the signal QUERY. For example, the servers  102   a - 102   n  may communicate with the camera systems  104   a - 104   n  to determine the capabilities of the computer vision operations (e.g., determine what types of objects  160   a - 160   n  and/or characteristics  162  may be detected). The detection engine  124  may convert the type of objects  160   a - 160   n  and/or characteristics  162  that may be detected by the camera systems  104   a - 104   n  into fields that may be stored as part of the data for the web interface  126 . When the property owner wants to list the property  50 , the servers  102   a - 102   n  may generate fields for the property listing form interface  402  that may be specific to the capabilities of the camera systems  104   a - 104   n  used by the property owner and/or the characteristics of the room/mounting location of the camera systems  104   a - 104   n.    
     The property listing form interface  402  may comprise a heading  404 , date fields  406   a - 406   b,  a location field  408 , a price field  410 , a number of restriction fields  412   a - 412   n,  a button  420  and/or a button  422 . The heading  404  may indicate that the property owner may list the property  50  for rent using the property listing form interface  402 . The various input fields  406   a - 412   n  may accept input from the user that may be converted to both the text version  252  and/or the machine readable version  254  of the rental agreement  122 . The machine readable version  254  may be communicated to the camera systems  104   a - 104   n  as the signal FEAT_SET. The property listing form interface  402  shown may be a representative example and the style, layout and/or available input fields may be varied according to the design criteria of a particular implementation. 
     The date input fields  406   a - 406   b  may comprise a start and end time for the rental of the property  50  (e.g., a time range for the rental offer). For example, the date input fields  406   a - 406   b  may be used to enter the time period  362   a - 362   b  of the rental agreement  122  shown in association with  FIG.  7   . The location input field  408  may accept a location (e.g., an address) of the property  50 . For example, the location input field  408  may be used to enter the address  360  of the rental agreement  122  shown in association with  FIG.  7   . The price input field  410  may accept a price for renting the location  50 . In the example shown, the price input field  410  may be on a per day basis. Other price options may be available (e.g., flat rate, per week, per month, etc.). 
     In some embodiments, the location input field  408  may also comprise input fields for the property owner to input data (e.g., technical specifications, room/mounting location characteristics and/or an identification number) for the camera systems  104   a - 104   n.  In some embodiments, the property listing form interface  402  may be modified based on the capabilities of the camera systems  104   a - 104   n  used by the property owner. As advancements are made to the computer vision detection capabilities of the camera systems  104   a - 104   n,  the property owner may have more options available (e.g., more input fields  412   a - 412   n  may be accessible). In an example, when the user inputs an address into the location field  408 , the servers  102   a - 102   n  may generate the signal QUERY to the camera systems  104   a - 104   n  at the location  50  to determine the capabilities of the camera systems  104   a - 104   n  and then update the property listing form interface  402  (e.g., based on the available parameters, features and/or types of detection that may be performed by the camera systems  104   a - 104   n ). For example, the signal QUERY may be used to determine the number of cameras  364  and/or the camera locations  366   a - 366   c  of the rental agreement  122  shown in association with  FIG.  7   . 
     The restriction input fields  412   a - 412   n  may enable the property owner to define the terms of the rental agreement  122 . In an example, the restriction input fields  412   a - 412   n  may be used as data for the entries  370   a - 370   n  of the rental agreement  122  shown in association with  FIG.  7   . In some embodiments, the number and/or types of the restriction input fields  412   a - 412   n  may be determined based on data generated by the detection engine  124  in response to the signal QUERY and/or the neural networks available in the detection engine  124 . The number, type and/or format of the input used for the restriction input fields  412   a - 412   n  may be varied according to the design criteria of a particular implementation. 
     In the example shown, the restriction input field  412   a  may comprise a maximum number of guests allowed. The property owner may restrict the number of people allowed on the property  50 , then signal FEAT_SET may provide the camera systems  104   a - 104   n  the maximum number of people that may be detected before a breach is determined to occur. For example, the restriction input field  412   a  may correspond to the entry  370   a  shown in association with  FIG.  7   . In response to the data input in the restriction input field  412   a,  the computer vision operations may be performed by the camera systems  104   a - 104   n  when the rental agreement  122  is active to detect if more than the maximum number of people have been detected. 
     In the example shown, the restriction input field  412   b  may be whether pets are allowed on the property  50 . In the example shown, the restriction input field  412   b  may be a binary choice (e.g., to allow pets or not). In some embodiments, the restriction input field  412   b  may enable the property owner to define how many pets are allowed and/or the type(s) of pets allowed. For example, a property owner that is allergic to cats may allow dogs in the rental agreement  122  but not cats. In an example, the restriction input field  412   b  may correspond to the entry  370   c  shown in association with  FIG.  7   . 
     In the example shown, the restriction input field  412   c  may comprise whether loud noises (e.g., music, movies, live performances, party guests, etc.) are allowed at the property  50 . In the example shown, the restriction input field  412   c  may comprise radio buttons for selecting particular times when loud noises may be allowed. For example, loud noises may be permitted before particular times to avoid noise complaints and/or avoid disturbing neighbors. However, any type of time input may be implemented by the rental listing web interface  402 . In an example, the restriction input field  412   c  may correspond to the maximum audio level entry  370   d  shown in association with  FIG.  7   . For example, the maximum audio level may not be applied before midnight based on the selection chosen for the restriction input field  412   c.  In some embodiments, an audio maximum audio level may be defined to indicate what audio level may be considered a loud noise. 
     In the example shown, the restriction input field  412   d  may comprise whether social gatherings are permitted. For example, the renter may be permitted to allow guests (e.g., have a party) but only during particular times periods. For example, the maximum number of guests may limit how many people are allowed overnight or past a particular time, but during other times more people may be allowed on the property  50 . In an example, the restriction input field  412   d  may correspond to the number of visitors allowed entry  370   b  shown in association with  FIG.  7   . The social gatherings may be defined as the number of extra guests that may be allowed at particular times. 
     In the example shown, the restriction input field  412   n  may comprise a free-form text field. The restriction input field  412   n  may enable the property owner to enter various other terms and/or conditions for renting the property  50 . In some embodiments, the restriction input field  412   n  may be further populated in response to the signal QUERY. 
     The types of restriction input fields  412   a - 412   n  available may be varied according to the design criteria of a particular implementation. For example, the property owner may provide restrictions on whether alcohol is allowed on the property  50 . In another example, the property owner may restrict access to particular locations on the property  50  (e.g., bedrooms, pool access, etc.). In yet another example, the property owner may define whether fireworks are allowed on the property  50 , whether only men or only women are allowed on the property  50 , the types of events allowed on the property  50 , etc. 
     The button  420  may provide a cancel function (e.g., to delete the data entered in the fields  406   a - 412   n  and/or leave the property listing form interface  402 ). The button  422  may provide a submit function to enable the property owner to use the data entered on the property listing form interface  402 . For example, when the property owner interacts with the list rental button  422  (e.g., clicks, taps, etc.) the user device  52   a  may communicate the signal RENTOFR to the servers  102   a - 102   n.  The processors  110   a - 110   n  of the servers  102   a - 102   n  may generate the listings  120  based on the data provided in the input fields  406   a - 412   n.    
     Referring to  FIG.  9   , a diagram illustrating an example interface for a rental request is shown. An example scenario  450  is shown. The example scenario  450  may comprise a view of the web browser  80 . Similar to the example scenario  400  shown in association with  FIG.  8   , the tab  82  and the URL  84  are shown. In the example scenario  450 , the tab  82  may comprise a website title indicating Renter Details. The web page displayed by the browser  80  may be the web interface  126 . In an example, the prospective renter user device  52   b  may access the servers  102   a - 102   n  using the web browser  80 . The servers  102   a - 102   n  may generate the signal WEB to present the web interface  126  to the prospective renter user device  52   b.    
     The web interface  126  generated by the servers  102   a - 102   n  shown may be a rental request form interface  452 . For example, the data from the web interface  126  stored by the servers  102   a - 102   n  may be used to provide data that the browser  80  may use to output the rental request form interface  452 . The rental request form interface  452  may be an example website shown to the prospective renter. The rental request form interface  452  may enable the prospective renter to search the listings  120  to find a suitable rental. 
     The rental request form interface  452  may provide various fields to accept input from the prospective renter. The fields may correspond to terms and/or conditions that may be used to create the rental agreement  122 . In some embodiments, the fields may be generated in response to the listings  120  based on the data provided by the property owner in the signal RENTOFR. For example, the rental request form interface  452  may be available after the property owner has provided the signal RENTOFR to communicate information about the property  50  to be stored as the listings  120 . When the prospective renter wants to search for a rental property, the servers  102   a - 102   n  may generate fields to enable a search of the property listing  122 . The servers  102   a - 102   n  may search the listings  120  based on the information received based on the input of the rental request form interface  452 . 
     The rental request form interface  452  may comprise a heading  454 , date fields  456   a - 456   b,  a location field  458 , a price field  460 , a number of feature fields  462   a - 462   n,  a button  470  and/or a button  472 . The heading  454  may indicate that the prospective renter may search the listings  120  based on the wants/needs of the renter using the rental request form interface  452 . In one example, the various input fields  456   a - 462   n  may accept input from the user that may be converted to both the text version  252  and/or the machine readable version  254  of the rental agreement  122 . In another example, the various input fields  456   a - 462   n  may accept input from the user that may be used by the servers  102   a - 102   n  to compare against the listings to return a closest match (or a number of the listings  120  that may be generally close to the input by the prospective renter). The rental request form interface  452  shown may be a representative example and the style, layout and/or available input fields may be varied according to the design criteria of a particular implementation. 
     The rental request form interface  452  may enable the prospective renter to enter criteria for a property to rent that may be listed in the listings  120 . The web interface may provide the various fields  456   a - 462   n  to accept input from the user device  52   b.  The fields  456   a - 462   n  may accept input from the user that may be converted to search criteria that may be used to filter the available listings  120 . The content available on the rental request form interface  452  for finding a rental property may be similar to the input fields  406   a - 412   n  used for the property listing form interface  402  shown in association with  FIG.  8    to enable a comparison to the data of the listings  120 . 
     The date input fields  456   a - 456   b  may comprise a desired start and end time for a desired rental (e.g., a time range for the rental request). For example, the date input fields  456   a - 456   b  may be used to compare to the listings  120  against the data entered in the date input fields  406   a - 406   b.  The location input field  458  may accept a location (e.g., an address, a general region, a city, etc.) for a desired rental. For example, the location input field  458  may be used to compare to the listings  120  against the data entered in the location input field  408 . The price input field  460  may accept a desired price point for a desired rental. For example, the price input field  460  may be used to compare to the listings  120  against the data entered in the price input field  410 . 
     The feature input fields  462   a - 462   n  may enable the prospective renter to define the terms of the rental agreement  122 . In an example, the feature input fields  462   a - 462   n  may be used as data for the entries  370   a - 370   n  of the rental agreement  122  shown in association with  FIG.  7   . In another example, the feature input fields  462   a - 462   n  may be used to search the listings  120  by comparing against the similar data entered for the restriction input fields  412   a - 412   n.  For example, the prospective renter may want to search for a rental property in a particular location that allows loud music late in order to throw a party. By comparing against the data entered with the restriction fields  412   a - 412   n,  the server computers  102   a - 102   n  may filter out available properties that are outside the desired region and/or available properties that do not allow loud music. 
     The prospective renters use the rental request form web interface  452  to search for venues at particular locations, for particular dates, in a particular price range that may suit the desires of the renter. For example, if the prospective renter is planning a wedding for two hundred guests, by providing a guest number to the appropriate one of the feature input fields  462   a - 462   n,  the servers  102   a - 102   n  may filter out rental listings  120  that do not meet the criteria of the prospective renter. The number, type and/or format of the input used for the feature input fields  462   a - 462   n  may be varied according to the design criteria of a particular implementation. 
     In the example shown, the feature input field  462   a  may comprise a maximum number of guests requested. The requested number of guests may be compared against the listings  120  (e.g., the maximum number of guests defined from the restriction field  412   a ). In the example shown, the restriction input field  462   b  may be whether a property that allows pets is requested. The request for a pet may be compared against the listings  120  (e.g., the number and/or types of pets defined from the restriction field  412   b ). Similar to the restriction field  412   b,  the pet feature input  462   b  may provide options for number and/or types of pets. In some embodiments, the pet feature input  462   b  may request properties that have never had pets (e.g., for severe allergies). 
     In the example shown, the feature input field  462   c  may comprise whether social gatherings are requested. In the example shown, a binary choice is shown. In some embodiments, the feature input field  462   c  may provide further input details such as number of extra guests, the time of day that the extra guests may be present, etc. The requested number of guests and/or times for the extra guests may be compared against the listings  120  (e.g., against the number and/or times for social gatherings defined in the restriction input  412   d ). In the example shown, the feature input field  462   d  may comprise whether loud noises (e.g., music, movies, live performances, party guests, etc.) are requested. In the example shown, the feature input field  462   d  may comprise radio buttons for selecting particular times when loud noises may be requested. Similar to the noise restriction input field  412   c,  the loud noise feature input  462   d  may provide further options such as maximum noise level requested. The requested loud music/movies may be compared against the listings  120  (e.g., against the times and/or maximum noise level defined in the restriction input  412   c ). 
     In the example shown, the feature input field  462   n  may comprise a free-form text field. The feature input field  462   n  may enable the prospective renter to enter various other features requested, terms and/or conditions for renting a property from the listings  120 . In some embodiments, the prospective renter may not define the terms and/or conditions of the rental agreement  122 . In some embodiments, the prospective renter may be provided an additional web interface to negotiate one or more terms of the rental agreement  122 . 
     The button  470  may provide a cancel function (e.g., to delete the data entered in the fields  456   a - 462   n  and/or to leave the rental request interface  452 ). The button  472  may provide a find a match button to enable the prospective renter to submit the data entered in the rental request interface  452  to the servers  102   a - 102   n.  For example, when the prospective renter interacts with the find a match button  472  (e.g., clicks, taps, etc.) the user device  52   b  may communicate the signal RENTREQ to the servers  102   a - 102   n.  The processors  110   a - 110   n  of the servers  102   a - 102   n  may search the listings  120  based on the data provided in the input fields  456   a - 462   n.  In some embodiments, the servers  102   a - 102   n  may provide a filtered list of suitable listings in response to the signal RENTREQ. In some embodiments, the servers  102   a - 102   n  may provide the rental agreement  122  in response to the signal RENTREQ (e.g., the property owner may list a property and automatically accept offers that fit the criteria of the listing  120 ). The processors  110   a - 110   n  may be configured to compare the data from the signal RENTOFR (e.g., stored in the listings  120 ) to the data from the signal RENTOFR to find data that matches and/or data that provides some degree of commonality. In some embodiments, the signal RENTOFR may comprise negotiation terms sent by the prospective renter (e.g., the prospective renter may find a listing  120  that is close to the desired property but may not allow enough people, the renter may negotiate by asking if more people may be allowed). 
     Referring to  FIG.  10   , a diagram illustrating an example interface for a rental agreement is shown. An example scenario  500  is shown. The example scenario  500  may comprise a view of the web browser  80 . Similar to the example scenario  400  shown in association with  FIG.  8   , the tab  82  and the URL  84  are shown. In the example scenario  500 , the tab  82  may comprise a website title indicating Rental Agreement. The web page displayed by the browser  80  may be the web interface  126 . In an example, the user devices  52   a - 52   b  may access the servers  102   a - 102   n  using the web browser  80 . The servers  102   a - 102   n  may generate the signal RAGREE to present the web interface  126  to the user devices  52   a - 52   b.    
     The web interface  126  generated by the servers  102   a - 102   n  shown may be a rental agreement form interface  502 . For example, the data from the web interface  126 , the listings  120  and/or the rental agreement  122  stored by the servers  102   a - 102   n  may be used to provide data that the browser  80  may use to output the rental agreement form interface  502 . The rental agreement form interface  502  may be an example website shown to both the property owner and the prospective renter. In one example, the rental agreement form interface  502  may be presented to the prospective renter in response to the signal RENTREQ. The servers  102   a - 102   n  may present the rental agreement form interface  502  when the rental request from the prospective renter from the signal RENTREQ matches (or has commonalities with) the one or more of the listings  120 . The rental agreement form interface  502  may enable the property owner and/or the prospective renter to agree to the rental agreement  122 . The rental agreement form interface  502  shown may be a representative example and the style, layout and/or available input fields may be varied according to the design criteria of a particular implementation. 
     In the example shown, the rental agreement form interface  502  may be an example of a matched listing provided in response to the rental request RENTREQ. An image  504  is shown. The image  504  may be an image of the property  50  that corresponds to the matched listing. In an example, the image  504  may be submitted by the property owner with the signal RENTOFR. In some embodiments, the image  504  may be an image captured by one or more of the camera systems  104   a - 104   n.  In some embodiments, the image  504  may be an image selected by the property owner. The rental agreement form interface  502  may comprise a property description  506 . 
     The property description  506  may comprise a list price  508 . The list price  508  may be determined based on the data from the listings  120  (e.g., as defined by the price input  410 ). The property description  506  may comprise statistics  510 . The statistics  510  may provide descriptive data about the location  50  (e.g., size, number of rooms, number of beds, amenities, etc.). The property description  506  may comprise a button  514  and/or additional details  516 . The additional details  516  may provide further information about the location  50  (e.g., directions, nearby restaurants, how old the property is, etc.). 
     The button  514  may be a messaging button. In the example shown, the messaging button  514  may enable the prospective renter to directly contact the property owner. Similarly, the property owner may use the messaging button  514  to directly contact the prospective renter. The messaging button  514  may provide an interface for direct contact (e.g., email, text message, video link, etc.). The messaging button  514  may enable the parties to negotiate terms of the rental agreement  122 . 
     In an example, one of the listings  120  may have a restriction of a maximum of five guests. The renter may use the messaging button  514  to send a request to the property owner to ask if the rental agreement  122  could be modified to allow seven guests. The property owner may have the option to agree to the modification, deny the modification and/or provide a counter offer. In an example, the property owner may counter by allowing up to six guests. In another example, the property owner may deny the modification and provide a reason (e.g., allowing more guests may be against the law). The messaging button  514  may enable negotiation and/or modification of the restrictions provided by the property owner in the signal RENTOFR. 
     The rental agreement form interface  502  may have a contract agreement section  520 . In the example shown, the contract agreement section  520  may indicate that a match was found between the requests of the prospective renter and the listings  120 . In the example, one match is shown. However, when the prospective renter provides the signal RENTREQ, the servers  102   a - 102   n  may return multiple properties from the listings  120  that generally fit the criteria of the signal RENTREQ. The contract agreement section  520  may comprise the text version  252  of the rental agreement  122 , a checkbox  522  and a button  524 . The checkbox  522  may be an agreement checkbox. The button  522  may be a confirmation button. 
     The rental agreement  122  displayed on the contract agreement section  520  may provide the text-based (e.g., human readable) version  252  listing of the terms and conditions  370   a - 370   n  of the rental agreement  122 . In the example shown, the text version  252  of the rental agreement  122  may indicate the entry  370   a  for the maximum number of guests allowed on the rental property  50 , the entry  370   b  for the times that loud noises are permitted, the entry  370   c  for the number and/or type of pets allowed, etc. Other terms and conditions  370   a - 370   n  may be listed (e.g., how loud the noise may be, the types of activities permitted, the type of noise permitted, etc.). 
     In some embodiments, the rental agreement  122  may comprise the terms and conditions  370   a - 370   n  as input by the property owner using the property listing form interface  402 . For example, the servers  102   a - 102   n  may convert the data input in the restriction input fields  412   a - 412   n  received in the signal RENTOFR into the entries  370   a - 370   n  of the rental agreement  122 . In the example shown, the rental agreement web interface  502  may be provided to the prospective renter. The renter may use the messaging button  514  to negotiate the terms  370   a - 370   n.  Similarly, the rental agreement interface  502  may be provided to the property owner to enable the property owner to agree to the terms  370   a - 370   n  of the rental agreement  122 . 
     In the example shown, the terms  370   a - 370   n  may comprise restrictions that may be imposed on the renter if the rental agreement  122  is agreed to. In some embodiments, the rental agreement  122  may indicate various requirements and/or guidelines for the property owner. In an example, the terms  370   a - 370   n  may outline the types of amenities that the property owner may make available. For example, the rental agreement  122  may indicate how many beds are to be made available. In another example, the rental agreement  122  may indicate whether the property owner is charging for usage of utilities. In yet another example, the rental agreement  122  may indicate whether various conveniences may be provided (e.g., towels, silverware, etc.). The property owner may use the messaging button  514  to negotiate the amenities provided. For example, if the prospective renter asks for additional guests to be allowed, the property owner may counter-offer with a higher price. 
     The checkbox  522  may be configured to enable the users to agree to the rental agreement  122 . Checking the checkbox  522  may enable the users to confirm and agree to the rental agreement  122 . The checkbox  522  may be used as an indication that each user has read the text version  252 , understands and agrees to the terms of the rental agreement  122 . When the checkbox  522  is checked by the user, the rent it button  524  may become accessible. Interacting with the rent it button  524  may submit a confirmation of the rental agreement  122  to the servers  122   a - 122   n.  In an example, when the property owner uses the user device  52   a  to check the checkbox  522  and clicks on the rent it button  524 , the user device  52   a  may communicate the confirmation signal CONFO to the servers  102   a - 102   n.  The signal CONFO may provide an indication that the property owner has agreed to the rental agreement  122 . In another example, when the renter uses the user device  52   b  to check the checkbox  522  and clicks on the rent it button  524 , the user device  52   b  may communicate the confirmation signal CONFR to the servers  102   a - 102   n.  The signal CONFR may provide an indication that the renter has agreed to the rental agreement  122 . 
     When the servers  102   a - 102   n  receive both the confirmation signal CONFO and the confirmation signal CONFR, the rental agreement  122  may be considered agreed to. In response to receiving the confirmation signal CONFO and the confirmation signal CONFR, the memory  112   a - 112   n  may store the rental agreement  122 . The rental agreement  122  may be parsed by the detection engine  124 . The detection engine  124  may determine the feature set based on the entries  370   a - 370   n  of the rental agreement  122 . The detection engine  124  may generate the machine readable version  254  of the rental agreement  122 . 
     The machine readable version  254  may be generated based on data about the camera systems  104   a - 104   n  at the location  50  in the rental agreement  122  determined from the signal QUERY. The machine readable version  254  of the rental agreement  122  may be communicated to the camera systems  104   a - 104   n  as the signal FEAT_SET. The camera systems  104   a - 104   n  may generate the detection parameters of the rental agreement  122  based on the machine readable version  254 . The camera systems  104   a - 104   n  may perform the computer vision and/or audio analysis of the location  50  when the rental time range  362   a - 362   b  is active. 
     In some embodiments, the restrictions provided by the rental offer interface  402 , the terms and/or conditions that may be negotiated, and/or the entries  370   a - 370   n  that may be available for the rental agreement  122  may be limited. For example, the entries  370   a - 370   n  that may be generated by the servers  102   a - 102   n  may generally correspond to the types of detection that the camera systems  104   a - 104   n  may perform. In some embodiments, the entries  370   a - 370   n  may be pre-defined parameters. For example, an overly complicated system of negotiating the terms  370   a - 370   n  may be not desired by end users. For example, the entries  370   a - 370   n  may be entered using the restriction input fields  412   a - 412   m.  Additional terms and conditions may be entered in the restriction free-form input field  412   n  but the free-form data may not be used as the basis for generating the feature set for the camera systems  104   a - 104   n  (e.g., other portions of the rental agreement  122  that may not be used by the camera systems  104   a - 104   n  may be generated by the free-form data). 
     In some embodiments, the detection engine  124  may convert the entries  370   a - 370   n  to the feature set used for the computer vision by the camera systems  104   a - 104   n.  The entries  370   a - 370   n  may be used as a basis for comparison with the output from the object/person/pet detection (and recognition) performed by the camera systems  104   a - 104   n  using the neural network(s). In one example, the detection parameters used by the camera systems  104   a - 104   n  may comprise a single network capable of recognizing various objects, people and/or pets. In another example, the detection parameters used by the camera systems  104   a - 104   n  may comprise multiple neural networks running in parallel. In a scenario that there are multiple neural networks, some of the neural networks might not be loaded if not applicable to the entries  370   a - 370   n.  For example, a pet recognition neural network may not be loaded if the entries  370   a - 370   n  do not provide a policy regarding pets. 
     The detection parameters may comprise the criteria for determining a breach of the rental agreement  122 . In an example, if one rental agreement  122  provides a limitation of five guests and another rental agreement  122  provides a limitation of six guests, the neural networks used for detecting people may be the same in each scenario. The criteria used on the output of the neural networks (e.g., the extracted data  170   a - 170   n ) may be different. For example, the neural network may detect six people and the output (e.g., the extracted data  170   a - 107   n  that indicates six people were detected) may be compared to the detection parameters that indicate a criteria of five people, which may indicate a breach of the rental agreement  122 . While the type of detection (e.g., people detection and counting) may be the same, the criteria used to determine a breach of the rental agreement  122  may be different. The entries  370   a - 370   n  generated by agreeing to the rental agreement  122  using the rental agreement interface  502  may be used by the detection engine  124  to determine the feature set to provide to the camera systems  104   a - 104   n.  The feature set may comprise the neural networks to detect the objects and/or the criteria for comparing against the extracted data  170   a - 170   n.    
     Referring to  FIG.  11   , a method (or process)  550  is shown. The method  550  may generate a rental agreement from user input. The method  550  generally comprises a step (or state)  552 , a step (or state)  554 , a step (or state)  556 , a step (or state)  558 , a step (or state)  560 , a step (or state)  562 , a step (or state)  564 , a step (or state)  566 , a step (or state)  568 , a decision step (or state)  570 , a step (or state)  572 , a step (or state)  574 , a step (or state)  576  and a step (or state)  578 . 
     The step  552  may start the method  550 . In the step  554 , the servers  102   a - 102   n  may enable the property listing form interface  402  to be generated for listing the location  50  for rent. In an example, data for the property listing form interface  402  may be stored in the web interface data  126 . The servers  102   a - 102   n  may communicate the data to the user device  52   a  via the signal WEB. The browser  80  of the user device  52   a  may interpret the data to generate the property listing form interface  402 . Next, in the step  556 , the servers  102   a - 102   n  may receive the rental offer information. For example, the property owner may use the property listing form interface  402  to input data into the fields  406   a - 412   n.  The property owner may submit the data to the servers  102   a - 102   n  as the signal RENTOFR. In the step  558 , the processors  110   a - 110   n  may receive the information in the signal RENTOFR and generate listing information. The listing information may be stored in the memory  112   a - 112   n  as the listings  120 . Next, the method  550  may move to the step  560 . 
     In the step  560 , the servers  102   a - 102   n  may enable the rental request interface  452  to be generated for viewing the stored rental listings  120 . In an example, data for the rental request interface  452  may be stored in the web interface data  126 . The servers  102   a - 102   n  may communicate the data to the user device  52   b  via the signal WEB. The browser  80  of the user device  52   b  may interpret the data to generate the rental request interface  452 . Next, in the step  562 , the servers  102   a - 102   n  may receive the rental request information. For example, the renter may use the rental request interface  452  to input data into the fields  456   a - 462   n.  The renter may submit the data to the servers  102   a - 102   n  as the signal RENTREQ. Next, the method  550  may move to the step  564 . 
     In the step  564 , the servers  102   a - 102   n  may match the data from the signal RENTREQ to the stored listings  120 . In one example, the servers  102   a - 102   n  may receive the signal RENTREQ, compare the rental request information to the listings  120 , and find an exact match. In another example, the servers  102   a - 102   n  may perform the comparison and return a number of the listings  120  that may generally correspond to the data in the signal RENTREQ (e.g., based on proximity to the location of the property and the request, based on the price range, based on the requested features and requested restrictions, etc.). The prospective renter may receive one match and/or select a desired choice from a subset of the listings  120  selected by the servers  102   a - 102   n  in response to the signal RENTREQ. Next, the method  550  may move to the step  566 . 
     In the step  566 , the servers  102   a - 102   n  may communicate the signal RAGREE to the user devices  52   a - 52   b  (e.g., to both the renter and the property owner). In an example, the users may agree to the rental agreement  122 . Next, in the step  568 , the servers  102   a - 102   n  may enable communication between the users (e.g., the renter and the property owner). In one example, the servers  102   a - 102   n  may enable the users to communicate to negotiate the entries  370   a - 370   n  of the rental agreement  122 . In another example, the servers  102   a - 102   n  may not allow the renter to negotiate the terms  370   a - 370   n  (e.g., the property owner may indicate that the terms are non-negotiable). Generally, the rental agreement interface  502  may enable the users to negotiate and/or agree to the terms of the rental agreement  122 . Next, the method  550  may move to the decision step  570 . 
     In the decision step  570 , the servers  102   a - 102   n  may determine whether the users have agreed to the rental agreement  122 . For example, the property owner may submit an acceptance using the rental agreement interface  502  and the user device  52   a  may communicate the signal CONFO. Similarly, the renter may submit an acceptance using the rental agreement interface  502  and the user device  52   b  may communicate the signal CONFR. The rental agreement  122  may be agreed to when the servers  102   a - 102   n  receive both the signal CONFO and the signal CONFR. If the rental agreement has not been agreed to, the method  550  may move to the step  578 . For example, the rental agreement  122  may not be formed. If the rental agreement  122  is agreed to, the method  550  may move to the step  572 . 
     In the step  572 , the processors  110   a - 110   n  may parse the terms and conditions of the rental agreement  122  submitted and/or agreed to in the signal RENTREQ and the signal RENTOFR to generate and store the rental agreement  122 . Next, in the step  574 , the detection engine  124  may generate the machine readable version  254  of the rental agreement  122 . The machine readable version  254  may be generated based on the entries  370   a - 370   n  of the rental agreement  122 . The machine readable version  254  may be the feature set for the camera systems  104   a - 104   n.  In the step  576 , the servers  102   a - 102   n  may communicate the feature set for the conditions generated by the detection engine  124  to the camera systems  104   a - 104   n  of the rental property  50 . In an example, the feature set may be communicated as the signal FEAT_SET. Next, the method  550  may move to the step  578 . The step  578  may end the method  550 . 
     Referring to  FIG.  12   , a method (or process)  600  is shown. The method  600  may load a feature set to the camera system when the rental agreement is active. The method  600  generally comprises a step (or state)  602 , a step (or state)  604 , a decision step (or state)  606 , a step (or state)  608 , a step (or state)  610 , a step (or state)  612 , a step (or state)  614 , a decision step (or state)  616 , a decision step (or state)  618 , and a step (or state)  620 . 
     The step  602  may start the method  600 . In the step  604 , the camera systems  104   a - 104   n  may receive the feature set from the servers  102   a - 102   n.  In an example, the camera systems  104   a - 104   n  may receive the signal FEAT_SET comprising the machine readable version  254  of the rental agreement  122 . Next, the method  600  may move to the decision step  606 . 
     In the decision step  606 , the processor  130  may determine whether the current time corresponds to the rental agreement  122 . In an example, the processor  130  may compare the current time to rental time range  362   a - 362   b  to determine whether the rental agreement  122  is currently in force. If the current time does not correspond to the rental time range  362   a - 362   b,  then the method  600  may move to the step  608 . In the step  608 , the camera systems  104   a - 104   n  may perform default camera functionality. For example, when the rental agreement  122  is not in force, the camera systems  104   a - 104   n  may operate according to various parameters to perform general functionality (e.g., general surveillance, video recording, video streaming, video encoding, etc.). Next, the method  600  may return to the decision step  606 . In the decision step  606 , if the current time does correspond to the rental time range  362   a - 362   n,  then the method  600  may move to the step  610 . 
     In the step  610 , the processor  130  may read the feature set of the machine readable version  254  of the rental agreement  122 . Next, in the step  612 , the processor  130  may convert the feature set to detection parameters. In an example, the dedicated hardware modules  240   a - 240   n  may generate and/or store the directed acyclic graph, and/or configure the various hardware modules  240   a - 240   n  to efficiently perform the computer vision operations specific to the machine readable version  254  of the rental agreement  122 . In the step  614 , the camera systems  104   a - 104   n  may perform the computer vision operations on the video frames  270   a - 270   n  based on the detection parameters. Next, the method  600  may move to the decision step  616 . 
     In the decision step  616 , the processor  130  may determine whether the rental agreement  122  has ended. For example, the processor  130  may compare to the current time to the rental end time value  362   b.  If the rental agreement  122  has not ended, then the method  600  may return to the step  614 . If the rental agreement has ended, then the method  600  may move to the decision step  618 . 
     In the decision step  618 , the camera systems  104   a - 104   n  may determine whether a new rental agreement is available. For example, a new rental agreement  122  may be communicated by the servers  102   a - 102   n  and/or the camera systems  104   a - 104   n  may store multiple rental agreements that may be active at different times. If no new rental agreement  122  is available, then the method  600  may move to the step  620 . In the step  620 , the camera systems  104   a - 104   n  may perform the default camera functionality. Next, the method  600  may return to the decision step  618 . For example, the camera systems  104   a - 104   n  may operate according to the default camera functionality until a new rental agreement  122  becomes available. In the decision step  618 , if a new rental agreement  122  is available, then the method  600  may return to the step  604  to receive the new feature set (or return to the decision step  606  if the new rental agreement  122  is already stored by the camera system  104   a - 104   n ). 
     Referring to  FIG.  13   , a method (or process)  650  is shown. The method  650  may perform the computer vision analysis of the video frames to detect a breach of the rental agreement. The method  650  generally comprises a step (or state)  652 , a step (or state)  654 , a step (or state)  656 , a step (or state)  658 , a step (or state)  660 , a step (or state)  662 , a step (or state)  664 , a step (or state)  666 , a decision step (or state)  668 , a step (or state)  670 , and a step (or state)  672 . 
     The step  652  may start the method  650 . In the step  654 , the processor  130  may load the detection parameters based on the feature set in the machine readable version  254  of the rental agreement  122 . For example, the processor  130  may configure the dedicated hardware modules  240   a - 240   n  based on the signal FEAT_SET. Next, in the step  656 , the capture devices  202   a - 202   n  may capture pixel data of the rental property  50 . For example, the image sensors  230   a - 230   n  may generate the signals PIXELD_A-PIXELD_N in response to the light received by the lenses  200   a - 200   n.  In the step  658 , the processor  130  may generate the video frames  270   a - 270   n  in response to the pixel data PIXELD_A-PIXELD_N. Next, the method  650  may move to the step  660 . 
     In the step  660 , the processor  130  may perform the computer vision operations on the video frames  270   a - 270   n.  For example, the computer vision modules  260  may perform the computer vision operations on the video frames  270   a - 270   n  using the dedicated hardware modules  240   a - 240   n.  Next, in the step  662 , the computer vision modules  260  may generate the extracted data  170  about the characteristics  162  of the objects  160   a - 160   n  detected in the video frames  270   a - 270   n.  Next, in the step  664 , after all the extracted data  170  (e.g., video and audio data) has been extracted from one of the video frames  270   a - 270   n,  the video and audio corresponding to the video frame may be discarded. For example, the video frames  270   a - 270   n  may be analyzed for data extraction and then, after the data extraction has been completed, sent to the discard function  262  to discard the data. Discarding the video frames  270   a - 270   n  and the audio data corresponding to the video frames  270   a - 270   n  after the data has been extracted may preserve the privacy of the people  70   a - 70   n,  while still enabling the processor  130  to have sufficient data to detect a breach of the rental agreement  122 . In the step  666 , the processor  130  may compare the extracted data  170  to the detection parameters of the machine readable version  254  of the rental agreement  122 . Next, the method  650  may move to the decision step  668 . 
     In the decision step  668 , the processor  130  may determine whether the rental agreement  122  has been breached. For example, the processor  130  may compare the extracted data  170  (e.g., the detection  354   a - 354   d  shown in association with  FIG.  7   ) to the detection parameters derived from the entries  370   a - 370   n.  If the processor  130  determines that the rental agreement  122  has not been breached, then the method  650  may return to the step  656 . If the processor  130  determines that the rental agreement has been breached, then the method  650  may move to the step  670 . 
     In the step  670 , the processor  130  may generate text from the extracted data  170  to describe the detected breach of the rental agreement  122 . In one example, if the breach corresponds to detecting music above the maximum audio level, the processor  130  may generate the message  310  (e.g., “a breach has been detected, please lower the volume”), with the detection  314  (e.g., “75 dB audio detected”), the condition  316  (e.g., “the maximum audio level is 70 dB”) and the timestamp  318  (e.g., “time of detection 3 AM”). Next, in the step  672 , the communication device  204  may communicate the notification (e.g., the signal NTF) to the users. For example, the notification signal NTF may be communicated to the property owner user device  52   a,  the renter user device  52   b  and/or the user devices  52   a - 52   n  of other parties of interest to the rental agreement  122 . Next, the method  650  may return to the step  656 . 
     Referring to  FIG.  14   , a method (or process)  700  is shown. The method  700  may generate a web-interface based on available features of the camera system. The method  700  generally comprises a step (or state)  702 , a step (or state)  704 , a step (or state)  706 , a step (or state)  708 , a step (or state)  710 , a step (or state)  712 , a step (or state)  714 , a step (or state)  716 , a decision step (or state)  718 , and a step (or state)  720 . 
     The step  702  may start the method  700 . In the step  704 , the servers  102   a - 102   n  may receive information about the camera systems  104   a - 104   n.  For example, the servers  102   a - 102   n  may communicate the signal QUERY to the camera systems  104   a - 104   n  at the location  50  owned by the property owner. The signal QUERY may request specifications about the capabilities of the camera systems  104   a - 104   n.  The camera systems  104   a - 104   n  may communicate the signal QUERY in return to provide the requested specifications and/or capabilities. Next, in the step  706 , the detection engine  124  may determine the available conditions (e.g., available parameters) for a feature set based on the information about the specifications/capabilities of the camera systems  104   a - 104   n.  For example, the detection engine  124  may determine what the camera systems  104   a - 104   n  are capable of detecting (e.g., one camera system may be capable of only detecting people but not pets, another camera may be configured to count people but not capable of detecting various characteristics about the people detected, etc.). Next, the method  700  may move to the step  708 . 
     In the step  708 , the processors  110   a - 110   n  may parse the available conditions. The processors  110   a - 110   n  may parse the available conditions based on the type of objects and/or characteristics that may be detected by the camera systems  104   a - 104   n  (e.g., convert the limitations and/or features of the camera systems  104   a - 104   n  into conditions that may be used as the entries  370   a - 370   n  of the rental agreement  122 ). Next, in the step  710 , the processors  110   a - 110   n  may generate the pre-defined restriction input fields  412   a - 412   n  that correspond to the conditions available based on the capabilities/features of the camera systems  104   a - 104   n.  Next, the method  700  may move to the step  712 . 
     In the step  712 , the processors  110   a - 110   n  may generate the web-interface  126  with the pre-defined restriction input fields  412   a - 412   n  for the rental offer. For example, the servers  102   a - 102   n  may communicate the signal WEB to the property owner user device  52   a  to provide the rental offer interface  402 . Next, in the step  714 , the servers  102   a - 102   n  may receive the rental offer from the user device  52   a  (e.g., the signal RENTOFR). In the step  716 , the servers  102   a - 102   n  may store the rental offer as one of the listings  120  and provide the listings  120  to prospective renters. Next, the method  700  may move to the decision step  718 . 
     In the decision step  718 , the servers  102   a - 102   n  may determine whether the camera systems  104   a - 104   n  at the location  50  have changed. In one example, the servers  102   a - 102   n  may periodically query the camera systems  104   a - 104   n  to determine whether the capabilities/features have changed. In another example, when a new one of the camera systems  104   a - 104   n  is installed at the location  50 , the camera systems  104   a - 104   n  may update the capabilities/features of the camera systems  104   a - 104   n  at the location  50 . If one or more of the camera systems  104   a - 104   n  have changed, then the method  700  may return to the step  704 . If one or more of the camera systems  104   a - 104   n  have not changed, then the method  700  may move to the step  720 . The step  720  may end the method  700 . 
     Referring to  FIG.  15   , a method (or process)  750  is shown. The method  750  may communicate pre-defined sets of neural networks to the camera systems based on the position of the camera systems and/or the terms of the rental agreement. The method  750  generally comprises a step (or state)  752 , a step (or state)  754 , a step (or state)  756 , a step (or state)  758 , a decision step (or state)  760 , a step (or state)  762 , a step (or state)  764 , and a step (or state)  766 . 
     The step  752  may start the method  750 . In the step  754 , the detection engine  124  my parse the rental agreement  122 . Next, in the step  756 , the detection engine  124  may query the characteristics of the camera systems  104   a - 104   n.  In an example, the detection engine  124  may communicate the signal QUERY to the camera systems  104   a - 104   n  that correspond to the rental agreement  122 . The camera systems  104   a - 104   n  may return the signal QUERY to provide the characteristics of the camera systems  104   a - 104   n.  In the step  758 , the detection engine  124  may determine the objects to detect from the rental agreement  122  (e.g., detecting pets, people, types of items, etc.). Next, the method  750  may move to the decision step  760 . 
     In the decision step  760 , the detection engine  124  may determine whether there are more neural networks to load for the feature set for the camera systems  104   a - 104   n.  The neural networks to be loaded may be determined based on the entries  370   a - 370   n  of the rental agreement  122 . More than one neural network may be loaded (e.g., one neural network for detecting people, one neural network for detecting pets, one neural network for detecting inanimate objects, etc.). If there are more neural networks to load, then the method  750  may move to the step  762 . In the step  762 , the detection engine  124  may compare the objects to detect from the rental agreement  122  to the camera characteristics determined from the signal QUERY. Next, in the step  764 , the detection engine  124  may communicate an appropriate one of the neural networks for the objects of the rental agreement  122  and the room characteristics of the camera systems  104   a - 104   n.  For example, if the rental agreement  122  comprises entries  370   a - 370   n  for detecting people and the room characteristics for one of the camera systems  104   a - 104   n  is that the camera is mounted from above, a neural network that comprises feature set libraries for detecting people from above may be selected. In another example, if the rental agreement  122  comprises entries  370   a - 370   n  for detecting people and the room characteristics for one of the camera systems  104   a - 104   n  is that the camera is mounted at a level that captures a side-view of people (e.g., as shown in association with  FIG.  2   ), a neural network that comprises feature set libraries for detecting people from body height level may be selected. Next, the method  750  may return to the decision step  760 . 
     In the decision step  760 , if there are no more neural networks to load, then the method  750  may move to the step  766 . In an example, there may be no additional neural networks to load when the camera systems  104   a - 104   n  have received neural networks that correspond to detecting the criteria of each of the entries  370   a - 370   n  in the rental agreement  122 . The step  766  may end the method  750 . 
     The characteristics of the camera systems  104   a - 104   n  may comprise technical specifications about the camera systems  104   a - 104   n  (e.g., resolution, shutter speed, focal length, zoom settings, frame rate, etc.). The characteristics of the camera systems  104   a - 104   n  may further comprise information about the environment that the camera systems  104   a - 104   n  are located (e.g., room characteristics). For example, the characteristics of the camera systems  104   a - 104   n  may comprise information about where the camera systems  104   a - 104   n  are mounted (e.g., how high up, how large the room is, the lighting in the room, expected distance from objects, etc.). For example, the detection parameters for detecting objects from different distances and/or in different lighting environments may be different. 
     The objects to detect from the rental agreement  122  may be determined based on the entries  370   a - 370   n.  For example, the detection parameters may be different for detecting different types of objects. In one example, the entries  370   a - 370   n  may comprise multiple entries for detecting people (e.g., number of people, number of visitors, activities of the visitors, etc.). To detect people, one neural network may be communicated to the camera systems  104   a - 104   n.  In another example, the entries  370   a - 370   n  may comprise entries for detecting pets. To detect pets, another neural network may be communicated to the camera systems  104   a - 104   n.  In some embodiments, different types of pets may each be detected based on separate neural networks (e.g., one neural network for detecting cats, another neural network for detecting dogs, another neural network for detecting lizards, etc.). In yet another example, the entries  370   a - 370   n  may comprise entries for detecting items and/or the conditions of items. Additional neural networks may be communicated to the camera systems  104   a - 104   n  for detecting items (e.g., detecting TVs, detecting types of damage to TVs, detecting a pool, detecting the usage of a pool, etc.). 
     The types of objects detected maybe combined with the location characteristics (e.g., room characteristics) of the camera systems  104   a - 104   n.  For example, one neural network may be implemented for detecting people from above and another neural network may be implemented for detecting people from ground level. In some embodiments, the detection engine  124  may not communicate neural networks that do not apply to the entries  370   a - 370   n  of the rental agreement  122 . For example, if none of the entries  370   a - 370   n  of the rental agreement  122  applies to pets (e.g., all pets are allowed), then the detection engine  124  may not communicate the neural networks that correspond to detecting animals to the camera systems  104   a - 104   n.  The number and/or types of neural networks that may be communicated as the feature set may be varied according to the design criteria of a particular implementation. 
     Since the camera systems  104   a - 104   n  may not store the video frames  270   a - 270   n  or communicate the video frames  270   a - 270   n,  the video frames  270   a - 270   n  may not be sent back to the servers  102   a - 102   n  for ongoing training of neural networks. The memory  112   a - 112   n  may store pre-defined sets of data (e.g., neural networks) that may be downloaded into the camera systems  104   a - 104   n.  The neural networks stored by the servers  102   a - 102   n  may be trained using training data acquired from sources other than the camera systems  104   a - 104   n  while the rental agreements  122  are active. The neural networks stored by the servers  102   a - 102   n  selected for the camera systems  104   a - 104   n  may be selected based on the position of the camera systems  104   a - 104   n  in the room and/or the size of the room at the location  50 . For example, some of the neural networks may be trained for small rooms and other neural networks may be trained for large rooms In another example, some of the neural networks may be trained for various heights of the camera systems  104   a - 104   n  in the rooms at the location  50  and/or the lighting in the room at the location  50 . For example, during installation and/or a set up time of the camera systems  104   a - 104   n,  the camera systems  104   a - 104   n  may detect the physical parameters of the room and communicate the physical parameters (e.g., room characteristics) back to the servers  102   a - 102   n.  The servers  102   a - 102   n  may select the neural network parameters optimized for a room according to the room characteristics. 
     The functions performed by the diagrams of  FIGS.  1 - 15    may be implemented using one or more of a conventional general purpose processor, digital computer, microprocessor, microcontroller, RISC (reduced instruction set computer) processor, CISC (complex instruction set computer) processor, SIMD (single instruction multiple data) processor, signal processor, central processing unit (CPU), arithmetic logic unit (ALU), video digital signal processor (VDSP) and/or similar computational machines, programmed according to the teachings of the specification, as will be apparent to those skilled in the relevant art(s). Appropriate software, firmware, coding, routines, instructions, opcodes, microcode, and/or program modules may readily be prepared by skilled programmers based on the teachings of the disclosure, as will also be apparent to those skilled in the relevant art(s). The software is generally executed from a medium or several media by one or more of the processors of the machine implementation. 
     The invention may also be implemented by the preparation of ASICs (application specific integrated circuits), Platform ASICs, FPGAs (field programmable gate arrays), PLDs (programmable logic devices), CPLDs (complex programmable logic devices), sea-of-gates, RFICs (radio frequency integrated circuits), ASSPs (application specific standard products), one or more monolithic integrated circuits, one or more chips or die arranged as flip-chip modules and/or multi-chip modules or by interconnecting an appropriate network of conventional component circuits, as is described herein, modifications of which will be readily apparent to those skilled in the art(s). 
     The invention thus may also include a computer product which may be a storage medium or media and/or a transmission medium or media including instructions which may be used to program a machine to perform one or more processes or methods in accordance with the invention. Execution of instructions contained in the computer product by the machine, along with operations of surrounding circuitry, may transform input data into one or more files on the storage medium and/or one or more output signals representative of a physical object or substance, such as an audio and/or visual depiction. The storage medium may include, but is not limited to, any type of disk including floppy disk, hard drive, magnetic disk, optical disk, CD-ROM, DVD and magneto-optical disks and circuits such as ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable ROMs), EEPROMs (electrically erasable programmable ROMs), UVPROMs (ultra-violet erasable programmable ROMs), Flash memory, magnetic cards, optical cards, and/or any type of media suitable for storing electronic instructions. 
     The elements of the invention may form part or all of one or more devices, units, components, systems, machines and/or apparatuses. The devices may include, but are not limited to, servers, workstations, storage array controllers, storage systems, personal computers, laptop computers, notebook computers, palm computers, cloud servers, personal digital assistants, portable electronic devices, battery powered devices, set-top boxes, encoders, decoders, transcoders, compressors, decompressors, pre-processors, post-processors, transmitters, receivers, transceivers, cipher circuits, cellular telephones, digital cameras, positioning and/or navigation systems, medical equipment, heads-up displays, wireless devices, audio recording, audio storage and/or audio playback devices, video recording, video storage and/or video playback devices, game platforms, peripherals and/or multi-chip modules. Those skilled in the relevant art(s) would understand that the elements of the invention may be implemented in other types of devices to meet the criteria of a particular application. 
     The terms “may” and “generally” when used herein in conjunction with “is(are)” and verbs are meant to communicate the intention that the description is exemplary and believed to be broad enough to encompass both the specific examples presented in the disclosure as well as alternative examples that could be derived based on the disclosure. The terms “may” and “generally” as used herein should not be construed to necessarily imply the desirability or possibility of omitting a corresponding element. 
     While the invention has been particularly shown and described with reference to embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made without departing from the scope of the invention.