Patent Publication Number: US-2023143370-A1

Title: Feature selection for object tracking using motion mask, motion prediction, or both

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application No. 63/277,838, filed on Nov. 10, 2021, the contents of which are incorporated by reference herein. 
    
    
     BACKGROUND 
     Many properties are equipped with monitoring systems that include sensors and connected system components. Some residential-based monitoring systems include cameras. 
     SUMMARY 
     Some residents and homeowners equip their properties with monitoring systems to enhance the security, safety, or convenience of their properties. A property monitoring system can include cameras that can obtain visual images of scenes at the property. A camera can be incorporated into a component of the property monitoring system, such as a doorbell. 
     A camera of a monitoring system can detect objects and track object movement within a field of view. For example, a doorbell camera with a field of view that includes a front yard of a property can track movement of objects, e.g., humans or pets, in the front yard. In some examples, movement detected by doorbell cameras can trigger a property monitoring system to perform one or more actions. For example, movements of humans that meet pre-programmed criteria may trigger the property monitoring system to send a notification to a resident of the property or to adjust a setting of the property monitoring system. Example criteria can include a human approaching the property within a certain range or at a certain speed, a threshold number of humans approaching the property, a human approaching the property late at night, or a combination of these. 
     A camera of a monitoring system can capture images of a scene and identify objects, or targets, to be tracked within the field of view. In some examples, a target can be a human, an animal, or a vehicle. A camera can use video tracking to associate targets in consecutive video images, or frames. For example, a doorbell camera can use video tracking to identify a location of one or more targets in a frame, and to predict locations of the targets in a subsequent frame. To perform object tracking, a camera can include an object detector and an object tracker. In some examples, the object detector and the object tracker can run on a computing system within the camera, can transfer video data to an external computing system, or a combination of both. 
     A camera may perform object tracking by identifying bounding boxes around objects within frames. The bounding boxes can include feature points within the bounding boxes. A feature point may be one or more points in an image that are mapped to a coordinate system. Values of pixels at or around the feature point can be matched to values of pixels in another image. 
     For example, a feature point may be mapped to a coordinate at [x, y] in an image, where x can represent a horizontal number of pixels and y can represent a vertical number of pixels. The pixels at or around the coordinate [x, y] can include, for example, a 3-by-3 square of pixels. Values of the pixels can include red, green, and blue pixel values. An object tracker can attempt to match the red, green, and blue pixel values in the 3-by-3 square of pixels with red, green, and blue values of another 3-by-3 square of pixels in another image. In this way, the feature points can be used for matching and comparing feature points of a first frame to feature points of a second frame. 
     In some examples, uniform grid points inside the bounding box may be used as feature points. However, these feature points from uniform grid points may include feature points generated from static background objects included in the bounding box, which may prevent reliable tracking of the objects. Accordingly, the doorbell camera may attempt to generate feature points from only portions of the bounding box that show motion. 
     Additionally or alternatively, an object may be attempted to be tracked by searching for feature points in an entirety of a next frame. However, searching an entirety of a next frame may be a computationally intensive process and introduce errors. Accordingly, the doorbell camera may attempt to reduce computations and errors by predicting the next position of the bounding box in the next frame, and then search for matches only in an area of the next frame using the prediction. 
     In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include the actions of obtaining a first image of a scene captured by a camera; identifying a bounding object around an object detected in the first image; identifying areas of motion in the first image; selecting first feature points that are within both the bounding object and the areas of motion in the first image; obtaining a second image of the scene captured by the camera after the first image was captured; and detecting the object in the second image using the first feature points. 
     Other implementations of this aspect include corresponding computer systems, apparatus, computer program products, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. 
     The foregoing and other implementations can each optionally include one or more of the following features, alone or in combination. Detecting the object in the second image includes: identifying, in the second image, second feature points that satisfy a similarity threshold for the first feature points in the first image; and detecting the object in the second image using the identified second feature points. The actions include determining whether a motion prediction of the object is available that indicates an area of the second image where the object is likely located; and in response to determining that the motion prediction of the object is available, searching, in the area of the second image where the object is likely located, for the second feature points that satisfy the similarity threshold for the first feature points in the first image. Identifying the areas of motion in the first image includes: obtaining a previous image of the scene captured by the camera before the first image was captured; determining differences between the first image and the previous image; and identifying the areas of motion in the first image using the differences. 
     In general, another innovative aspect of the subject matter described in this specification can be embodied in methods that include the actions of obtaining first feature points of an object in a first image of a scene captured by a camera; obtaining a second image of the scene captured by the camera after the first image was captured; determining whether a motion prediction of the object is available that indicates an area of the second image where the object is likely located; in response to determining that the motion prediction of the object is available, identifying, in the area of the second image where the object is likely located, second feature points that satisfy a similarity threshold for the first feature points in the first image; and detecting the object in the second image using the identified second feature points. 
     Other implementations of this aspect include corresponding computer systems, apparatus, computer program products, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. 
     The foregoing and other implementations can each optionally include one or more of the following features, alone or in combination. Obtaining the first feature points of the object in the first image includes: obtaining the first image of the scene captured by the camera; identifying a bounding object around the object in the first image; identifying areas of motion in the first image; and selecting the first feature points that are within both the bounding object and the areas of motion in the first image. The actions include generating the motion prediction of the object using a Kalman filter algorithm. The actions include generating the motion prediction of the object using the first image and one or more images captured by the camera before the first image was captured. 
     The subject matter described in this specification can be implemented in various implementations and may result in one or more of the following advantages. In some implementations, instead of using uniform grid feature points inside an object bounding box, the systems and methods described in this specification can select feature points inside a motion mask and can use the feature points inside the motion mask for more accurate tracking of an object. In some implementations, instead of searching for feature points in an entirety of a next frame, the systems and methods described in this specification can predict the next position of an object in the next frame, and then can search for matching feature points only in an area of the next position of the object in the next frame, reducing the computation resources necessary, improving the accuracy of the searching process, or both. 
     The details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example environment for object tracking using motion mask, motion prediction, or both. 
         FIG.  2    is a flow chart illustrating an example of a process for object tracking using a motion mask. 
         FIG.  3    is a flow chart illustrating an example of a process for object tracking using motion prediction. 
         FIG.  4    is a diagram illustrating an example of a property monitoring system. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG.  1    illustrates an example environment  100  for object tracking using motion mask, motion prediction, or both. In  FIG.  1   , a camera  102  is installed at a property  105 . The property  105  can be a home, another residence, a place of business, a public space, or another facility that has one or more cameras  102  installed. The camera  102  may be a doorbell camera that is a component of a doorbell  106  that is installed external to the property  105 . The doorbell  106  may be installed near a front door  115  of the property  105 . In some examples, the doorbell  106  can be a component of a property monitoring system that collects data from various sensors to monitor conditions and events at the property  105 . 
     In addition to the camera  102 , the doorbell  106  may include other components and sensors. For example, the doorbell  106  may include a button that, when depressed, causes an audible tone to sound at the property  105 . The doorbell  106  may also include additional sensors, e.g., a motion sensor, temperature sensor, light sensor, and a microphone. 
     The camera  102  captures video from a scene within a field of view. The video includes multiple sequential images, also referred to as frames. The video can include any type of images. For example, the video can include visual light images, infrared images, or radio wave images. In some examples, the video can include a combination of one or more types of images, e.g., visual light images with infrared illumination. 
     The field of view is an area that is observable by the camera  102 . The camera  102  has a field of view that includes the area in front of the property  105 . For example, the field of view can include a front yard, walkway, and street in front of the property  105 . 
     In some examples, the camera  102  can capture video continuously. In some examples, the camera  102  can capture video when triggered by an event. For example, the camera  102  may capture video when triggered by depression of the button on the doorbell  106 . In some examples, the camera  102  may capture video when triggered by activation of the motion sensor or other sensor of the doorbell  106 . 
     The camera  102  may capture video for a preprogrammed amount of time. For example, when triggered by depression of the button on the doorbell  106 , the camera  102  may capture video for a preprogrammed time of  10  seconds,  30  seconds, or  60  seconds. When triggered by a motion sensor, the camera  102  may capture video for a preprogrammed time, may capture video until the motion sensor no longer detects motion, or a combination of both. 
     The camera  102  can perform video analysis on captured video. Video analysis can include detecting, identifying, and tracking objects, or targets, in the video. The camera  102  includes an object detector  104  that can detect the presence of a target object within a frame. When the object detector  104  detects a target object, the object detector  104  can identify a bounding box around the image of the target object in the frame. The camera  102  includes an object tracker  114  that can track object movement from one frame to a next frame. The object tracker  114  can track the bounding boxes of existing target objects in each subsequent frame. 
     Operations of the object detector  104  may require more computation time than operations of the object tracker  114 . Therefore, the camera  102  may run the object detector  104  less frequently than the object tracker  114 . For example, the camera  102  may run the object detector  104  by providing captured frames to the object detector  104  every third frame, every fifth frame, every tenth frame, or at some other frequency. The camera  102  may run the object tracker  114  more frequently than the object detector  104 . For example, the camera  102  may provide captured frames to the object tracker  114  on every frame that the object detector  104  is not run on, or on every other frame that the object detector  104  is not run on. 
     In the example of  FIG.  1   , a visitor  120  approaches the front door  115  of the property  105 . The camera  102  captures a video that includes Frame 1. The camera  102  may capture the video including Frame 1, for example, upon one or more of being triggered by a motion sensor that detects the motion of the visitor  120 , as part of a constant capturing of frames, or upon a doorbell button being pressed. Frame 1 includes an image of the visitor  120 . Frame 1 also includes images of background objects, including a vehicle, a house, a tree, a street, and a walkway. 
     The object detector  104  receives Frame 1, including the image of the visitor  120 . The object detector  104  identifies the image of the visitor as a human target  122 . The object detector  104  identifies a bounding box  124  around the human target  122 . The bounding box  124  can be an area of the frame where the human target  122  is positioned. The bounding box  124  can coarsely outline the human target  122  using, for example, a rectangular shape with a height and a width. 
     The bounding box  124  includes the human target  122  within the perimeter of the bounding box  124 . The bounding box  124  also includes images of background objects within the perimeter of the bounding box  124 . For example, the bounding box  124  includes portions of images of the walkway and the street. The bounding box  124  excludes images of other background objects, including images of the vehicle, the house, and the tree. 
     The bounding box  124  includes feature point  126 . A feature point  126  may be one or more points in an image that are mapped to a coordinate system. Values of pixels at or around the feature point  126  can be matched to values of pixels in another image. The pixels at or around the feature point  126  can include, for example, a 3-by-3 square of pixels. Values of the pixels can include red, green, and blue pixel values. 
     An object tracker can attempt to match the red, green, and blue pixel values in the 3-by-3 square of pixels with red, green, and blue values of another 3-by-3 square of pixels in another image. Thus, the feature point  126  can be used for tracking feature points of objects between frames. In some implementations, the frame may include feature points that aren&#39;t within the bounding box  124 , but only feature points from within the bounding box may be used for matching to a subsequent frame. 
     In some examples, the feature points can be arranged in a uniform grid pattern within a bounding box. However, the selection of feature points that correspond to pixels that aren&#39;t part of the object to be tracked may cause tracking to fail. For example, if half the feature points show ground or sidewalk in the bounding box and the other half the feature points show part of the human target  122 , when the human target  122  moves, half of the feature points will move with the human target  122  and the other half the feature points that show ground or sidewalk will not move, and the object tracker may then be unable to generate a bounding box where the feature points all have the same relative positions from each other from a prior frame. 
     The object detector  104  outputs frames with bounding boxes to the feature selector  112 . For example, the object detector  104  outputs Frame 1 with the bounding box  124  to the feature selector  112 . 
     The motion mask generator  110  obtains frames from the camera  102  and generates a motion mask from the frames. For example, the motion mask generator  110  may receive Frame 1 and Frame 0, that was the frame captured by the camera  102  immediately before Frame 1 was captured, and generate the motion mask  130 . 
     The motion mask may include the same dimensions as the frames, and may have pixels that each includes a binary value that indicates whether motion was detected in the pixel. For example, the motion mask  130  may show a value of zero for a pixel in the upper left most corner to indicate there was no motion detected in that location in Frame 0 and Frame 1. In some examples, the motion mask  130  may show a value of one for a pixel in the center of the motion mask  130  as the human target  122  in the center of the image moved. As shown in  FIG.  1   , the motion mask  130  may show that the only motion between Frame 0 to Frame 1 was the human target  122  slightly moving and a tree in the upper right slightly moving. 
     The motion mask generator  110  may generate the motion mask by determining differences between two frames. For example, the motion mask generator  110  may generate the motion mask  130  by determining differences between Frame 0 and Frame 1. The motion mask generator  110  may generate the motion mask, including: converting frames in the red, green, blue (RGB) color space to the LUV color space, determining the differences between the luminance value for each pixel location between the two frames, and determining whether the difference satisfies a luminance criteria. 
     For example, the motion mask generator  110  may determine a luminance value of seventy for the center pixel in Frame 0, determine a luminance value of one hundred for the center pixel in Frame 1, determine the difference of thirty satisfies a threshold difference of ten, and set the value for the center pixel in the motion mask as one. In some examples, the motion mask generator  110  may determine a luminance value of three for the upper left most pixel in Frame 0, determine a luminance value of nine for the upper left most pixel in Frame 1, determine the difference of six does not satisfy a threshold difference of ten, and set the value for the center pixel in the motion mask as zero. 
     The motion mask generator  110  may provide the motion mask to the feature selector  112 . For example, the motion mask generator  110  may provide the motion mask  130  to the feature selector  112 . 
     The feature selector  112  may select feature points to use for tracking the object using the bounding box and the motion mask. For example, the feature selector  112  may select all the feature points in the bounding box  124  as represented by dots in  FIG.  1   , except for the upper left most, upper right most, and bottom right most. The feature selector  112  may select feature points that correspond to moving objects to avoid selecting feature points that correspond to non-moving background objects. 
     The feature selector  112  may select feature points by only selecting feature points from among the feature points that are both in the bounding box and at a location that the motion mask indicates motion. For example, the feature selector  112  may select the feature point in the center of the bounding box  124  because the feature point is both within the bounding box  124  and in a location that the motion mask indicates motion. 
     In some examples, the feature selector  112  may not select the feature point in the upper left most corner of the bounding box  124  because, while the feature point is within the bounding box  124 , the feature point is not in a location that the motion mask indicates motion. In some examples, the feature selector  112  may not select the feature point where the tree is shown because, while the motion mask indicates that location includes motion, the feature point is not within the bounding box  124 . 
     In some implementations, the feature selector  112  may determine, using an amount of motion shown in the bounding box, whether to select feature points using the motion mask. For example, the feature selector  112  may determine, based on 80% of the bounding box showing motion, to select feature points from only locations in the bounding box that show motion. In some examples, the feature selector  112  may determine, based on 20% of the bounding box showing motion, to select feature points from locations in the bounding box that may or may not show motion. 
     The feature selector  112  may determine, from determining whether a motion criteria is satisfied, whether to select feature points using the motion mask. For example, the feature selector  112  may determine that the motion mask indicates that more than 50% of the bounding box shows motion and, in response, determine to select only feature points within the bounding box that indicate motion. 
     In some examples, the feature selector  112  may determine that the motion mask indicates that less than 50% of the bounding box shows motion and, in response, determine to select feature points within the bounding box without further analysis of whether the motion mask indicates motion in various locations. The feature selector  112  may determine whether a motion criteria is satisfied to determine whether motion indicated by the motion mask is noise or is from actual motion of an object to be tracked. Noise may correspond to less motion in the bounding box and actual motion may correspond to more motion in the bounding box. 
     The camera  102  continues to capture subsequent frames, including Frame 2. Frame 2 includes an image of the visitor  120 . As the visitor  120  continues to approach the front door  115 , the appearance of the visitor  120  is expected to change between Frame 2 and Frame 1. For example, the visitor  120  is likely to get larger and show up in different pixel locations. 
     As described above, due to increased computation time, the camera  102  might not run the object detector  104  on Frame 2. The object tracker  114  tracks movement of the human target  122  between Frame 1 and Frame 2. The object tracker  114  can track movement of the human target  122 , for example, by searching Frame 2 for feature points that match the previously selected feature points. 
     The object tracker  114  receives the selected feature points and subsequent frame, and attempts to match each selected feature point to a feature point in the subsequent frame. For example, the object tracker  114  may attempt to match all the selected feature points from Frame 1 to feature points in Frame 2, where the relative positions of the selected feature points in Frame 1 are expected to be similar in the matching feature points in Frame 2. 
     The object tracker  114  locates the human target  122  in the Frame 2 using the identified feature points in Frame 2 and generates a bounding box around the human target  122 . The object tracker  114  outputs Frame 2 with the bounding box. The object tracker  114  can continue to track the human target  122  until the camera  102  runs the object detector  104  on a following frame, until the human target  122  no longer appears in the field of view, or a combination of both. 
     In some implementations, the object tracker  114  may include a motion predictor  116  that the object tracker  114  may use to attempt to more accurately track an object. Without the motion predictor  116 , the object tracker  114  may search for feature points that match selected feature points in an extended neighborhood centered where the object was previously detected. For example, the object tracker  114  may search only an area that is within five pixels of the previous bounding box. However, if there is another object that has the similar color appearance in the subsequent frame, the object tracker  114  may have difficulty determining which object needs to be matched. 
     The motion predictor  116  may limit the search area for the object tracker  114 . The motion predictor  116  may determine whether a motion prediction is available and, if available, the object tracker  114  may search for feature points only in a region of the frame where the motion prediction indicates the object will be, and, if not available, the object tracker  114  may search for feature points in the entire frame. 
     The motion predictor  116  may use a Kalman filter algorithm to generate motion prediction. For example, the motion predictor  116  may exploit a series of observation measurements (e.g. bounding box information such as center point (x, y) and width and height of the bounding box) over the length of object trajectory, containing statistical noise. The motion predictor  116  may estimate unknown variables (x, y, width, height) with statistical distribution by estimating a joint probability distribution over the variables for each timeframe. Given the observed target trajectory, the motion predictor  116  may compute the predicted trajectory in the next frame. The predicted trajectory may indicate the candidate regions of the bounding box in the next frame. The object tracker  114  may then search the candidate regions for the selected feature points. The candidate regions may be much smaller than the entire frame and can reduce the computation cost for the task of feature matching. 
     The motion predictor  116  may provide a prediction with sufficient history of locations of the target. For example, the motion predictor  116  may be able to provide a prediction if the tracked object was detected within at least the thirty most recent frames. The search area may include the bounding box around where the tracked object is predicted to be. For example, the search area may include an area centered at the center of the bounding box around where the tracked object is predicted to be, enlarged by 20%, 30%, or some other amount. 
     The camera  102  or the property monitoring system is an example of a system implemented as computer programs on one or more computers in one or more locations, in which the systems, components, and techniques described in this specification are implemented. The property monitoring system may include personal computers, mobile communication devices, and other devices that can send and receive data over a network. The network (not shown), such as a local area network (“LAN”), wide area network (“WAN”), the Internet, or a combination thereof, connects the devices of the property monitoring system, such as the camera  102 . The property monitoring system may use a single server computer or multiple server computers operating in conjunction with one another, including, for example, a set of remote computers deployed as a cloud computing service. 
     The property monitoring system, e.g., the camera  102 , can include several different functional components, including an object detector  104 , a motion mask generator  110 , a feature selector  112 , an object tracker  114 , and a motion predictor  116 . The object detector  104 , the motion mask generator  110 , the feature selector  112 , the object tracker  114 , or the motion predictor  116 , or a combination of these, can include one or more data processing apparatuses, can be implemented in code, or a combination of both. For instance, each of the object detector  104 , the motion mask generator  110 , the feature selector  112 , the object tracker  114 , and the motion predictor  116  can include one or more data processors and instructions that cause the one or more data processors to perform the operations discussed herein. 
     The various functional components of the property monitoring system, e.g., the camera  102 , may be installed on one or more computers as separate functional components or as different modules of a same functional component. For example, the components of the object detector  104 , the motion mask generator  110 , the feature selector  112 , the object tracker  114 , and the motion predictor  116  of the property monitoring system, e.g., the camera  102 , can be implemented as computer programs installed on one or more computers in one or more locations that are coupled to each through a network. In cloud-based systems for example, these components can be implemented by individual computing nodes of a distributed computing system. 
       FIG.  2    is a flow chart illustrating an example of a process  200  for object tracking using a motion mask. The process  200  can be performed by a camera, e.g. the camera  102 . In some implementations, the process  200  can be performed by one or more computer systems that communicate electronically with a camera, e.g., over a network. In some implementations, the process  200  can be performed by a property monitoring system. 
     The process  200  includes obtaining a first image of a scene captured by a camera ( 202 ). For example, the camera  102  may generate Frame 1. 
     The process  200  includes identifying a bounding object around an object detected in the first image ( 204 ). A bounding object can define an area around an object detected in the first image. In some implementations, the bounding object can be a bounding box around the object detected in the first image. In some implementations, the bounding object can be a circle, a square, a triangle, or another shape. For example, the object detector  104  may generate the bounding box  124  around the human target  122  shown in Frame 1. 
     The process  200  includes identifying areas of motion in the first image ( 206 ). In some implementations, identifying the areas of motion in the first image can include: obtaining a previous image of the scene captured by the camera before the first image was captured; determining differences between the first image and the previous image; and identifying the areas of motion in the first image using the differences. For example, the motion mask generator  110  may generate the motion mask  130  from Frame 1 and previously captured Frame 0. 
     The process  200  includes selecting first feature points within the bounding object and the areas of motion in the first image ( 208 ). For example, the feature selector  112  may select the feature point  126  that is within the bounding box  124  and indicated by the motion mask  130  as showing motion. 
     The process  200  includes obtaining a second image of the scene captured by the camera after the first image was captured ( 210 ). For example, the object tracker  114  may receive Frame 2 that was captured by the camera  102  after Frame 1 was captured. 
     The process  200  includes detecting the object in the second image using the selected feature points ( 212 ). For example, the object tracker  114  may detect the human target  122  in Frame 2 by matching the selected feature points from Frame 1 to feature points in Frame 2. 
     In some implementations, the system can use the selected feature points in other video analytics. For example, the system can use the selected feature points on the same object over time to learn a visual representation of the object according to the locations and descriptions of the feature points. The system can use the visual representation for downstream video analytics tasks, such as long-term person re-identification and forensic search. 
     In some implementations, detecting the object in the second image can include: identifying, in the second image, second feature points that satisfy a similarity threshold for the first feature points in the first image; and detecting the object in the second image using the identified second feature points. In some implementations, the second feature points in the second image that satisfy the similarity threshold for the first feature points can be the second feature points that “matches” the first feature points. 
     For example, the system can compute a score characterizing the differences between the pixel values of second feature points and the pixel values of the first feature points. The system can determine whether the score satisfies the similarity threshold, e.g., larger than a threshold value. If the score satisfies the similarity threshold, the system can identify the second feature points as feature points in the second image that “matches” the first feature points in the first image. If the score does not satisfy the similarity threshold, the system can search for other feature points in the second image that “matches” the first feature points. 
     In some implementations, the process  200  can include determining whether a motion prediction of the object is available that indicates an area of the second image where the object is likely located; and in response to determining that the motion prediction of the object is available, searching, in the area of the second image where the object is likely located, for the second feature points that satisfy the similarity threshold for the first feature points in the first image. 
     The order of steps in the process  200  described above is illustrative only, and the steps in the process  200  can be performed in different orders. For example, the step  210  can be performed before one or more of the steps  204 ,  206 , and  208 . In some implementations, the process  200  can include additional steps, fewer steps, or some of the steps can be divided into multiple steps. 
       FIG.  3    is a flow chart illustrating an example of a process  300  for object tracking using motion prediction. The process  300  can be performed by a camera, e.g., the camera  102 . In some implementations, the process  300  can be performed by one or more computer systems that communicate electronically with a camera, e.g., over a network. In some implementations, the process  200  can be performed by a property monitoring system. 
     The process  300  includes obtaining first feature points of an object in a first image of a scene captured by a camera ( 302 ). For example, the object tracker  114  may obtain feature points including a feature point  126  selected by the feature selector  112 . 
     In some implementations, obtaining the first feature points of the object in the first image can include: obtaining the first image of the scene captured by the camera; identifying a bounding object around the object in the first image; identifying areas of motion in the first image; and selecting the first feature points that are within both the bounding object and the areas of motion in the first image. 
     The process  300  includes obtaining a second image of the scene captured by the camera after the first image was captured ( 303 ). For example, the camera  102  can capture Frame 2 that includes an image of the visitor  120 . 
     The process  300  includes determining whether a motion prediction of the object is available that indicates an area of the second image where the object is likely located ( 304 ). In some implementations, the process  300  can include generating the motion prediction of the object using the first image and one or more images captured by the camera before the first image was captured. For example, the motion predictor  116  may determine whether the tracked object was detected in the thirty most recent frames and, if so, determine the motion prediction is available and, if not, determine the motion prediction is not available. 
     In some implementations, the process  300  can include generating the motion prediction of the object using a Kalman filter algorithm. For example, in Kalman filtering, the system can take an object motion model (e.g., a motion mask) and an observation model as input, and can predict the object location in the next frame using the object&#39;s location in the current frame. 
     The process  300  includes, in response to determining that the motion prediction of the object is available, identifying, in the area of the second image where the object is likely located, second feature points that satisfy a similarity threshold for the first feature points in the first image ( 308 ). In some implementations, if the motion prediction is available, the process  300  can include identifying, from a predicted area of the second image, second feature points that match the first feature points in the first image. For example, the object tracker  114  may match feature points from Frame 1 to feature points in Frame 2 that are in the predicted area (e.g., the area that the motion prediction indicates the tracked object will be) in Frame 2, and not other areas of Frame 2. 
     In some implementations, the process  300  can include, in response to determining that the motion prediction of the object is not available, identifying, in the second image, second feature points that satisfy a similarity threshold for the first feature points in the first image ( 306 ). For example, sometimes, there can be too many objects in the image and it may not be feasible to perform motion prediction, such as using the Kalman filters to predict the motion of each object. In some implementations, if the motion prediction is not available, the process  300  can include identifying, from an entirety of the second image, second feature points that match the first feature points in the first image. For example, the object tracker  114  may match feature points from Frame 1 to feature points in an entirety of Frame 2. 
     The process  300  includes detecting the object in the second image using the identified second feature points ( 310 ). For example, the object tracker  114  may detect the object in the second image in response to determining that there are matches between the obtained feature points from the first image and the feature points in the second image. 
     The order of steps in the process  300  described above is illustrative only, and the steps in the process  300  can be performed in different orders. In some implementations, the process  300  can include additional steps, fewer steps, or some of the steps can be divided into multiple steps. 
       FIG.  4    is a diagram illustrating an example of a property monitoring system  400 . The property monitoring system  400  includes a network  405 , a control unit  410 , one or more user devices  440  and  450 , a monitoring application server  460 , and a central alarm station server  470 . In some examples, the network  405  facilitates communications between the control unit  410 , the one or more user devices  440  and  450 , the monitoring application server  460 , and the central alarm station server  470 . 
     The network  405  is configured to enable exchange of electronic communications between devices connected to the network  405 . For example, the network  405  may be configured to enable exchange of electronic communications between the control unit  410 , the one or more user devices  440  and  450 , the monitoring application server  460 , and the central alarm station server  470 . The network  405  may include, for example, one or more of the Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a public switched telephone network (PSTN), Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (DSL)), radio, television, cable, satellite, or any other delivery or tunneling mechanism for carrying data. Network  405  may include multiple networks or subnetworks, each of which may include, for example, a wired or wireless data pathway. The network  405  may include a circuit-switched network, a packet-switched data network, or any other network able to carry electronic communications (e.g., data or voice communications). For example, the network  405  may include networks based on the Internet protocol (IP), asynchronous transfer mode (ATM), the PSTN, packet-switched networks based on IP, X.25, or Frame Relay, or other comparable technologies and may support voice using, for example, VoIP, or other comparable protocols used for voice communications. The network  405  may include one or more networks that include wireless data channels and wireless voice channels. The network  405  may be a wireless network, a broadband network, or a combination of networks including a wireless network and a broadband network. 
     The control unit  410  includes a controller  412  and a network module  414 . The controller  412  is configured to control a control unit monitoring system (e.g., a control unit system) that includes the control unit  410 . In some examples, the controller  412  may include a processor or other control circuitry configured to execute instructions of a program that controls operation of a control unit system. In these examples, the controller  412  may be configured to receive input from sensors, flow meters, or other devices included in the control unit system and control operations of devices included in the household (e.g., speakers, lights, doors, etc.). For example, the controller  412  may be configured to control operation of the network module  414  included in the control unit  410 . 
     The network module  414  is a communication device configured to exchange communications over the network  405 . The network module  414  may be a wireless communication module configured to exchange wireless communications over the network  405 . For example, the network module  414  may be a wireless communication device configured to exchange communications over a wireless data channel and a wireless voice channel. In this example, the network module  414  may transmit alarm data over a wireless data channel and establish a two-way voice communication session over a wireless voice channel. The wireless communication device may include one or more of a LTE module, a GSM module, a radio modem, a cellular transmission module, or any type of module configured to exchange communications in one of the following formats: LTE, GSM or GPRS, CDMA, EDGE or EGPRS, EV-DO or EVDO, UMTS, or IP. 
     The network module  414  also may be a wired communication module configured to exchange communications over the network  405  using a wired connection. For instance, the network module  414  may be a modem, a network interface card, or another type of network interface device. The network module  414  may be an Ethernet network card configured to enable the control unit  410  to communicate over a local area network and/or the Internet. The network module  414  also may be a voice band modem configured to enable the alarm panel to communicate over the telephone lines of Plain Old Telephone Systems (POTS). 
     The control unit system that includes the control unit  410  includes one or more sensors. For example, the monitoring system  400  may include multiple sensors  420 . The sensors  420  may include a lock sensor, a contact sensor, a motion sensor, or any other type of sensor included in a control unit system. The sensors  420  also may include an environmental sensor, such as a temperature sensor, a water sensor, a rain sensor, a wind sensor, a light sensor, a smoke detector, a carbon monoxide detector, an air quality sensor, etc. The sensors  420  further may include a health monitoring sensor, such as a prescription bottle sensor that monitors taking of prescriptions, a blood pressure sensor, a blood sugar sensor, a bed mat configured to sense presence of liquid (e.g., bodily fluids) on the bed mat, etc. In some examples, the health monitoring sensor can be a wearable sensor that attaches to a user in the property. The health monitoring sensor can collect various health data, including pulse, heart-rate, respiration rate, sugar or glucose level, bodily temperature, or motion data. The sensors  420  can include a radio-frequency identification (RFID) sensor that identifies a particular article that includes a pre-assigned RFID tag. 
     The control unit  410  communicates with the module  422  and a camera  430  to perform monitoring. The module  422  is connected to one or more devices that enable property automation, e.g., home or business automation. For instance, the module  422  may be connected to one or more lighting systems and may be configured to control operation of the one or more lighting systems. Also, the module  422  may be connected to one or more electronic locks at the property and may be configured to control operation of the one or more electronic locks (e.g., control Z-Wave locks using wireless communications in the Z-Wave protocol). Further, the module  422  may be connected to one or more appliances at the property and may be configured to control operation of the one or more appliances. The module  422  may include multiple modules that are each specific to the type of device being controlled in an automated manner. The module  422  may control the one or more devices based on commands received from the control unit  410 . For instance, the module  422  may cause a lighting system to illuminate an area to provide a better image of the area when captured by a camera  430 . The camera  430  can include one or more batteries  431  that require charging. 
     A drone  490  can be used to survey the electronic system  400 . In particular, the drone  490  can capture images of each item found in the electronic system  400  and provide images to the control unit  410  for further processing. Alternatively, the drone  490  can process the images to determine an identification of the items found in the electronic system  400 . 
     The camera  430  may be a video/photographic camera or other type of optical sensing device configured to capture images. For instance, the camera  430  may be configured to capture images of an area within a property monitored by the control unit  410 . The camera  430  may be configured to capture single, static images of the area or video images of the area in which multiple images of the area are captured at a relatively high frequency (e.g., thirty images per second) or both. The camera  430  may be controlled based on commands received from the control unit  410 . 
     The camera  430  may be triggered by several different types of techniques. For instance, a Passive Infra-Red (PIR) motion sensor may be built into the camera  430  and used to trigger the camera  430  to capture one or more images when motion is detected. The camera  430  also may include a microwave motion sensor built into the camera and used to trigger the camera  430  to capture one or more images when motion is detected. The camera  430  may have a “normally open” or “normally closed” digital input that can trigger capture of one or more images when external sensors (e.g., the sensors  420 , PIR, door/window, etc.) detect motion or other events. In some implementations, the camera  430  receives a command to capture an image when external devices detect motion or another potential alarm event. The camera  430  may receive the command from the controller  412  or directly from one of the sensors  420 . 
     In some examples, the camera  430  triggers integrated or external illuminators (e.g., Infra-Red, Z-wave controlled “white” lights, lights controlled by the module  422 , etc.) to improve image quality when the scene is dark. An integrated or separate light sensor may be used to determine if illumination is desired and may result in increased image quality. 
     The camera  430  may be programmed with any combination of time/day schedules, system “arming state”, or other variables to determine whether images should be captured or not when triggers occur. The camera  430  may enter a low-power mode when not capturing images. In this case, the camera  430  may wake periodically to check for inbound messages from the controller  412 . The camera  430  may be powered by internal, replaceable batteries, e.g., if located remotely from the control unit  410 . The camera  430  may employ a small solar cell to recharge the battery when light is available. The camera  430  may be powered by the controller&#39;s  412  power supply if the camera  430  is co-located with the controller  412 . 
     In some implementations, the camera  430  communicates directly with the monitoring application server  460  over the Internet. In these implementations, image data captured by the camera  430  does not pass through the control unit  410  and the camera  430  receives commands related to operation from the monitoring application server  460 . 
     The system  400  also includes thermostat  434  to perform dynamic environmental control at the property. The thermostat  434  is configured to monitor temperature and/or energy consumption of an HVAC system associated with the thermostat  434 , and is further configured to provide control of environmental (e.g., temperature) settings. In some implementations, the thermostat  434  can additionally or alternatively receive data relating to activity at a property and/or environmental data at a property, e.g., at various locations indoors and outdoors at the property. The thermostat  434  can directly measure energy consumption of the HVAC system associated with the thermostat, or can estimate energy consumption of the HVAC system associated with the thermostat  434 , for example, based on detected usage of one or more components of the HVAC system associated with the thermostat  434 . The thermostat  434  can communicate temperature and/or energy monitoring information to or from the control unit  410  and can control the environmental (e.g., temperature) settings based on commands received from the control unit  410 . 
     In some implementations, the thermostat  434  is a dynamically programmable thermostat and can be integrated with the control unit  410 . For example, the dynamically programmable thermostat  434  can include the control unit  410 , e.g., as an internal component to the dynamically programmable thermostat  434 . In addition, the control unit  410  can be a gateway device that communicates with the dynamically programmable thermostat  434 . In some implementations, the thermostat  434  is controlled via one or more module  422 . 
     A module  437  is connected to one or more components of an HVAC system associated with a property, and is configured to control operation of the one or more components of the HVAC system. In some implementations, the module  437  is also configured to monitor energy consumption of the HVAC system components, for example, by directly measuring the energy consumption of the HVAC system components or by estimating the energy usage of the one or more HVAC system components based on detecting usage of components of the HVAC system. The module  437  can communicate energy monitoring information and the state of the HVAC system components to the thermostat  434  and can control the one or more components of the HVAC system based on commands received from the thermostat  434 . 
     In some examples, the system  400  further includes one or more robotic devices  490 . The robotic devices  490  may be any type of robots that are capable of moving and taking actions that assist in security monitoring. For example, the robotic devices  490  may include drones that are capable of moving throughout a property based on automated control technology and/or user input control provided by a user. In this example, the drones may be able to fly, roll, walk, or otherwise move about the property. The drones may include helicopter type devices (e.g., quad copters), rolling helicopter type devices (e.g., roller copter devices that can fly and also roll along the ground, walls, or ceiling) and land vehicle type devices (e.g., automated cars that drive around a property). In some cases, the robotic devices  490  may be robotic devices  490  that are intended for other purposes and merely associated with the system  400  for use in appropriate circumstances. For instance, a robotic vacuum cleaner device may be associated with the monitoring system  400  as one of the robotic devices  490  and may be controlled to take action responsive to monitoring system events. 
     In some examples, the robotic devices  490  automatically navigate within a property. In these examples, the robotic devices  490  include sensors and control processors that guide movement of the robotic devices  490  within the property. For instance, the robotic devices  490  may navigate within the property using one or more cameras, one or more proximity sensors, one or more gyroscopes, one or more accelerometers, one or more magnetometers, a global positioning system (GPS) unit, an altimeter, one or more sonar or laser sensors, and/or any other types of sensors that aid in navigation about a space. The robotic devices  490  may include control processors that process output from the various sensors and control the robotic devices  490  to move along a path that reaches the desired destination and avoids obstacles. In this regard, the control processors detect walls or other obstacles in the property and guide movement of the robotic devices  490  in a manner that avoids the walls and other obstacles. 
     In addition, the robotic devices  490  may store data that describes attributes of the property. For instance, the robotic devices  490  may store a floorplan and/or a three-dimensional model of the property that enables the robotic devices  490  to navigate the property. During initial configuration, the robotic devices  490  may receive the data describing attributes of the property, determine a frame of reference to the data (e.g., a property or reference location in the property), and navigate the property based on the frame of reference and the data describing attributes of the property. Further, initial configuration of the robotic devices  490  also may include learning of one or more navigation patterns in which a user provides input to control the robotic devices  490  to perform a specific navigation action (e.g., fly to an upstairs bedroom and spin around while capturing video and then return to a property charging base). In this regard, the robotic devices  490  may learn and store the navigation patterns such that the robotic devices  490  may automatically repeat the specific navigation actions upon a later request. 
     In some examples, the robotic devices  490  may include data capture and recording devices. In these examples, the robotic devices  490  may include one or more cameras, one or more motion sensors, one or more microphones, one or more biometric data collection tools, one or more temperature sensors, one or more humidity sensors, one or more air flow sensors, and/or any other types of sensor that may be useful in capturing monitoring data related to the property and users in the property. The one or more biometric data collection tools may be configured to collect biometric samples of a person in the property with or without contact of the person. For instance, the biometric data collection tools may include a fingerprint scanner, a hair sample collection tool, a skin cell collection tool, and/or any other tool that allows the robotic devices  490  to take and store a biometric sample that can be used to identify the person (e.g., a biometric sample with DNA that can be used for DNA testing). 
     In some implementations, the robotic devices  490  may include output devices. In these implementations, the robotic devices  490  may include one or more displays, one or more speakers, and/or any type of output devices that allow the robotic devices  490  to communicate information to a nearby user. 
     The robotic devices  490  also may include a communication module that enables the robotic devices  490  to communicate with the control unit  410 , each other, and/or other devices. The communication module may be a wireless communication module that allows the robotic devices  490  to communicate wirelessly. For instance, the communication module may be a Wi-Fi module that enables the robotic devices  490  to communicate over a local wireless network at the property. The communication module further may be a  900  MHz wireless communication module that enables the robotic devices  490  to communicate directly with the control unit  410 . Other types of short-range wireless communication protocols, such as Bluetooth, Bluetooth LE, Z-wave, Zigbee, etc., may be used to allow the robotic devices  490  to communicate with other devices in the property. In some implementations, the robotic devices  490  may communicate with each other or with other devices of the system  400  through the network  405 . 
     The robotic devices  490  further may include processor and storage capabilities. The robotic devices  490  may include any suitable processing devices that enable the robotic devices  490  to operate applications and perform the actions described throughout this disclosure. In addition, the robotic devices  490  may include solid-state electronic storage that enables the robotic devices  490  to store applications, configuration data, collected sensor data, and/or any other type of information available to the robotic devices  490 . 
     The robotic devices  490  are associated with one or more charging stations. The charging stations may be located at predefined home base or reference locations in the property. The robotic devices  490  may be configured to navigate to the charging stations after completion of tasks needed to be performed for the property monitoring system  400 . For instance, after completion of a monitoring operation or upon instruction by the control unit  410 , the robotic devices  490  may be configured to automatically fly to and land on one of the charging stations. In this regard, the robotic devices  490  may automatically maintain a fully charged battery in a state in which the robotic devices  490  are ready for use by the property monitoring system  400 . 
     The charging stations may be contact based charging stations and/or wireless charging stations. For contact based charging stations, the robotic devices  490  may have readily accessible points of contact that the robotic devices  490  are capable of positioning and mating with a corresponding contact on the charging station. For instance, a helicopter type robotic device may have an electronic contact on a portion of its landing gear that rests on and mates with an electronic pad of a charging station when the helicopter type robotic device lands on the charging station. The electronic contact on the robotic device may include a cover that opens to expose the electronic contact when the robotic device is charging and closes to cover and insulate the electronic contact when the robotic device is in operation. 
     For wireless charging stations, the robotic devices  490  may charge through a wireless exchange of power. In these cases, the robotic devices  490  need only locate themselves closely enough to the wireless charging stations for the wireless exchange of power to occur. In this regard, the positioning needed to land at a predefined home base or reference location in the property may be less precise than with a contact based charging station. Based on the robotic devices  490  landing at a wireless charging station, the wireless charging station outputs a wireless signal that the robotic devices  490  receive and convert to a power signal that charges a battery maintained on the robotic devices  490 . 
     In some implementations, each of the robotic devices  490  has a corresponding and assigned charging station such that the number of robotic devices  490  equals the number of charging stations. In these implementations, the robotic devices  490  always navigate to the specific charging station assigned to that robotic device. For instance, a first robotic device may always use a first charging station and a second robotic device may always use a second charging station. 
     In some examples, the robotic devices  490  may share charging stations. For instance, the robotic devices  490  may use one or more community charging stations that are capable of charging multiple robotic devices  490 . The community charging station may be configured to charge multiple robotic devices  490  in parallel. The community charging station may be configured to charge multiple robotic devices  490  in serial such that the multiple robotic devices  490  take turns charging and, when fully charged, return to a predefined home base or reference location in the property that is not associated with a charger. The number of community charging stations may be less than the number of robotic devices  490 . 
     Also, the charging stations may not be assigned to specific robotic devices  490  and may be capable of charging any of the robotic devices  490 . In this regard, the robotic devices  490  may use any suitable, unoccupied charging station when not in use. For instance, when one of the robotic devices  490  has completed an operation or is in need of battery charge, the control unit  410  references a stored table of the occupancy status of each charging station and instructs the robotic device to navigate to the nearest charging station that is unoccupied. 
     The system  400  further includes one or more integrated security devices  480 . The one or more integrated security devices may include any type of device used to provide alerts based on received sensor data. For instance, the one or more control units  410  may provide one or more alerts to the one or more integrated security input/output devices  480 . Additionally, the one or more control units  410  may receive sensor data from the sensors  420  and determine whether to provide an alert to the one or more integrated security input/output devices  480 . 
     The sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the integrated security devices  480  may communicate with the controller  412  over communication links  424 ,  426 ,  428 ,  432 ,  438 ,  484 , and  486 . The communication links  424 ,  426 ,  428 ,  432 ,  438 ,  484 , and  486  may be a wired or wireless data pathway configured to transmit signals from the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , the drone  490 , and the integrated security devices  480  to the controller  412 . The sensors  420 , the module  422 , the camera  430 , the thermostat  434 , the drone  490 , and the integrated security devices  480  may continuously transmit sensed values to the controller  412 , periodically transmit sensed values to the controller  412 , or transmit sensed values to the controller  412  in response to a change in a sensed value. In some implementations, the drone  490  can communicate with the monitoring application server  460  over network  405 . The drone  490  can connect and communicate with the monitoring application server  460  using a Wi-Fi or a cellular connection. 
     The communication links  424 ,  426 ,  428 ,  432 ,  438 ,  484 , and  486  may include a local network. The sensors  420 , the module  422 , the camera  430 , the thermostat  434 , the drone  490  and the integrated security devices  480 , and the controller  412  may exchange data and commands over the local network. The local network may include  802 . 11  “Wi-Fi” wireless Ethernet (e.g., using low-power Wi-Fi chipsets), Z-Wave, Zigbee, Bluetooth, “HomePlug” or other “Powerline” networks that operate over AC wiring, and a Category 5 (CAT5) or Category 6 (CAT6) wired Ethernet network. The local network may be a mesh network constructed based on the devices connected to the mesh network. 
     The monitoring application server  460  is an electronic device configured to provide monitoring services by exchanging electronic communications with the control unit  410 , the one or more user devices  440  and  450 , and the central alarm station server  470  over the network  405 . For example, the monitoring application server  460  may be configured to monitor events (e.g., alarm events) generated by the control unit  410 . In this example, the monitoring application server  460  may exchange electronic communications with the network module  414  included in the control unit  410  to receive information regarding events (e.g., alerts) detected by the control unit  410 . The monitoring application server  460  also may receive information regarding events (e.g., alerts) from the one or more user devices  440  and  450 . 
     In some examples, the monitoring application server  460  may route alert data received from the network module  414  or the one or more user devices  440  and  450  to the central alarm station server  470 . For example, the monitoring application server  460  may transmit the alert data to the central alarm station server  470  over the network  405 . 
     The monitoring application server  460  may store sensor and image data received from the monitoring system  400  and perform analysis of sensor and image data received from the monitoring system  400 . Based on the analysis, the monitoring application server  460  may communicate with and control aspects of the control unit  410  or the one or more user devices  440  and  450 . 
     The monitoring application server  460  may provide various monitoring services to the system  400 . For example, the monitoring application server  460  may analyze the sensor, image, and other data to determine an activity pattern of a resident of the property monitored by the system  400 . In some implementations, the monitoring application server  460  may analyze the data for alarm conditions or may determine and perform actions at the property by issuing commands to one or more components of the system  400 , possibly through the control unit  410 . 
     The central alarm station server  470  is an electronic device configured to provide alarm monitoring service by exchanging communications with the control unit  410 , the one or more mobile devices  440  and  450 , and the monitoring application server  460  over the network  405 . For example, the central alarm station server  470  may be configured to monitor alerting events generated by the control unit  410 . In this example, the central alarm station server  470  may exchange communications with the network module  414  included in the control unit  410  to receive information regarding alerting events detected by the control unit  410 . The central alarm station server  470  also may receive information regarding alerting events from the one or more mobile devices  440  and  450  and/or the monitoring application server  460 . 
     The central alarm station server  470  is connected to multiple terminals  472  and  474 . The terminals  472  and  474  may be used by operators to process alerting events. For example, the central alarm station server  470  may route alerting data to the terminals  472  and  474  to enable an operator to process the alerting data. The terminals  472  and  474  may include general-purpose computers (e.g., desktop personal computers, workstations, or laptop computers) that are configured to receive alerting data from a server in the central alarm station server  470  and render a display of information based on the alerting data. For instance, the controller  412  may control the network module  414  to transmit, to the central alarm station server  470 , alerting data indicating that a sensor  420  detected motion from a motion sensor via the sensors  420 . The central alarm station server  470  may receive the alerting data and route the alerting data to the terminal  472  for processing by an operator associated with the terminal  472 . The terminal  472  may render a display to the operator that includes information associated with the alerting event (e.g., the lock sensor data, the motion sensor data, the contact sensor data, etc.) and the operator may handle the alerting event based on the displayed information. 
     In some implementations, the terminals  472  and  474  may be mobile devices or devices designed for a specific function. Although  FIG.  4    illustrates two terminals for brevity, actual implementations may include more (and, perhaps, many more) terminals. 
     The one or more user devices  440  and  450  are devices that host and display user interfaces. For instance, the user device  440  is a mobile device that hosts or runs one or more native applications (e.g., the smart property application  442 ). The user device  440  may be a cellular phone or a non-cellular locally networked device with a display. The user device  440  may include a cell phone, a smart phone, a tablet PC, a personal digital assistant (“PDA”), or any other portable device configured to communicate over a network and display information. For example, implementations may also include Blackberry-type devices (e.g., as provided by Research in Motion), electronic organizers, iPhone-type devices (e.g., as provided by Apple), iPod devices (e.g., as provided by Apple) or other portable music players, other communication devices, and handheld or portable electronic devices for gaming, communications, and/or data organization. The user device  440  may perform functions unrelated to the monitoring system, such as placing personal telephone calls, playing music, playing video, displaying pictures, browsing the Internet, maintaining an electronic calendar, etc. 
     The user device  440  includes a smart property application  442 . The smart property application  442  refers to a software/firmware program running on the corresponding mobile device that enables the user interface and features described throughout. The user device  440  may load or install the smart property application  442  based on data received over a network or data received from local media. The smart property application  442  runs on mobile devices platforms, such as iPhone, iPod touch, Blackberry, Google Android, Windows Mobile, etc. The smart property application  442  enables the user device  440  to receive and process image and sensor data from the monitoring system. 
     The user device  450  may be a general-purpose computer (e.g., a desktop personal computer, a workstation, or a laptop computer) that is configured to communicate with the monitoring application server  460  and/or the control unit  410  over the network  405 . The user device  450  may be configured to display a smart property user interface  452  that is generated by the user device  450  or generated by the monitoring application server  460 . For example, the user device  450  may be configured to display a user interface (e.g., a web page) provided by the monitoring application server  460  that enables a user to perceive images captured by the camera  430  and/or reports related to the monitoring system. Although  FIG.  4    illustrates two user devices for brevity, actual implementations may include more (and, perhaps, many more) or fewer user devices. 
     In some implementations, the one or more user devices  440  and  450  communicate with and receive monitoring system data from the control unit  410  using the communication link  438 . For instance, the one or more user devices  440  and  450  may communicate with the control unit  410  using various local wireless protocols such as Wi-Fi, Bluetooth, Z-wave, Zigbee, HomePlug (Ethernet over power line), or wired protocols such as Ethernet and USB, to connect the one or more user devices  440  and  450  to local security and automation equipment. The one or more user devices  440  and  450  may connect locally to the monitoring system and its sensors and other devices. The local connection may improve the speed of status and control communications because communicating through the network  405  with a remote server (e.g., the monitoring application server  460 ) may be significantly slower. 
     Although the one or more user devices  440  and  450  are shown as communicating with the control unit  410 , the one or more user devices  440  and  450  may communicate directly with the sensors and other devices controlled by the control unit  410 . In some implementations, the one or more user devices  440  and  450  replace the control unit  410  and perform the functions of the control unit  410  for local monitoring and long range/offsite communication. 
     In other implementations, the one or more user devices  440  and  450  receive monitoring system data captured by the control unit  410  through the network  405 . The one or more user devices  440 ,  450  may receive the data from the control unit  410  through the network  405  or the monitoring application server  460  may relay data received from the control unit  410  to the one or more user devices  440  and  450  through the network  405 . In this regard, the monitoring application server  460  may facilitate communication between the one or more user devices  440  and  450  and the monitoring system. 
     In some implementations, the one or more user devices  440  and  450  may be configured to switch whether the one or more user devices  440  and  450  communicate with the control unit  410  directly (e.g., through link  438 ) or through the monitoring application server  460  (e.g., through network  405 ) based on a location of the one or more user devices  440  and  450 . For instance, when the one or more user devices  440  and  450  are located close to the control unit  410  and in range to communicate directly with the control unit  410 , the one or more user devices  440  and  450  use direct communication. When the one or more user devices  440  and  450  are located far from the control unit  410  and not in range to communicate directly with the control unit  410 , the one or more user devices  440  and  450  use communication through the monitoring application server  460 . 
     Although the one or more user devices  440  and  450  are shown as being connected to the network  405 , in some implementations, the one or more user devices  440  and  450  are not connected to the network  405 . In these implementations, the one or more user devices  440  and  450  communicate directly with one or more of the monitoring system components and no network (e.g., Internet) connection or reliance on remote servers is needed. 
     In some implementations, the one or more user devices  440  and  450  are used in conjunction with only local sensors and/or local devices in a house. In these implementations, the system  400  includes the one or more user devices  440  and  450 , the sensors  420 , the module  422 , the camera  430 , and the robotic devices, e.g., that can include the drone  490 . The one or more user devices  440  and  450  receive data directly from the sensors  420 , the module  422 , the camera  430 , and the robotic devices and send data directly to the sensors  420 , the module  422 , the camera  430 , and the robotic devices. The one or more user devices  440 ,  450  provide the appropriate interfaces/processing to provide visual surveillance and reporting. 
     In other implementations, the system  400  further includes network  405  and the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices are configured to communicate sensor and image data to the one or more user devices  440  and  450  over network  405  (e.g., the Internet, cellular network, etc.). In yet another implementation, the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices are intelligent enough to change the communication pathway from a direct local pathway when the one or more user devices  440  and  450  are in close physical proximity to the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices to a pathway over network  405  when the one or more user devices  440  and  450  are farther from the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices. In some examples, the system leverages GPS information from the one or more user devices  440  and  450  to determine whether the one or more user devices  440  and  450  are close enough to the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices to use the direct local pathway or whether the one or more user devices  440  and  450  are far enough from the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices that the pathway over network  405  is required. In other examples, the system leverages status communications (e.g., pinging) between the one or more user devices  440  and  450  and the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices to determine whether communication using the direct local pathway is possible. If communication using the direct local pathway is possible, the one or more user devices  440  and  450  communicate with the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices using the direct local pathway. If communication using the direct local pathway is not possible, the one or more user devices  440  and  450  communicate with the sensors  420 , the module  422 , the camera  430 , the thermostat  434 , and the robotic devices using the pathway over network  405 . 
     In some implementations, the system  400  provides end users with access to images captured by the camera  430  to aid in decision-making. The system  400  may transmit the images captured by the camera  430  over a wireless WAN network to the user devices  440  and  450 . Because transmission over a wireless WAN network may be relatively expensive, the system  400  can use several techniques to reduce costs while providing access to significant levels of useful visual information (e.g., compressing data, down-sampling data, sending data only over inexpensive LAN connections, or other techniques). 
     In some implementations, a state of the monitoring system  400  and other events sensed by the monitoring system  400  may be used to enable/disable video/image recording devices (e.g., the camera  430 ). In these implementations, the camera  430  may be set to capture images on a periodic basis when the alarm system is armed in an “away” state, but set not to capture images when the alarm system is armed in a “stay” state or disarmed. In addition, the camera  430  may be triggered to begin capturing images when the alarm system detects an event, such as an alarm event, a door-opening event for a door that leads to an area within a field of view of the camera  430 , or motion in the area within the field of view of the camera  430 . In other implementations, the camera  430  may capture images continuously, but the captured images may be stored or transmitted over a network when needed. 
     The described systems, methods, and techniques may be implemented in digital electronic circuitry, computer hardware, firmware, software, or in combinations of these elements. Apparatus implementing these techniques may include appropriate input and output devices, a computer processor, and a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor. A process implementing these techniques may be performed by a programmable processor executing a program of instructions to perform desired functions by operating on input data and generating appropriate output. The techniques may be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. Each computer program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language may be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and Compact Disc Read-Only Memory (CD-ROM). Any of the foregoing may be supplemented by, or incorporated in, specially designed ASICs (application-specific integrated circuits). 
     It will be understood that various modifications may be made. For example, other useful implementations could be achieved if steps of the disclosed techniques were performed in a different order and/or if components in the disclosed systems were combined in a different manner and/or replaced or supplemented by other components. Accordingly, other implementations are within the scope of the disclosure.