Heat-based pattern recognition and event determination for adaptive surveillance control in a surveillance system

A method for heat-based control of a surveillance system is provided. The method may include detecting a first pattern of heat transferred from a heat source based on a first sensor dataset corresponding to a first event, determining an expected pattern of heat to be transferred from the heat source during a second event based on the first pattern of heat transfer, and generating a surveillance model based on the expected pattern of heat transfer. The method may further include detecting a second pattern of heat transferred from the heat source based on a second sensor dataset corresponding to the second event, detecting the heat source during the second event with respect to the second pattern of heat transfer, and determining a threat level corresponding to a security risk posed by the heat source with respect to the environment.

BACKGROUND

The present invention relates to heat-based pattern recognition and event detection in an automated surveillance system, and in particular to a method of adaptive surveillance control for targeted, context-based event detection in a monitored environment.

Video surveillance systems are widely applied in various residential, commercial, governmental, and industrial settings. For example, a surveillance system including video cameras positioned in an environment may be implemented to monitor and secure the environment based on captured images and video footage (i.e. surveillance data) of people and objects in the environment. The surveillance data may be communicated as a feed or stream across a network to a central location for monitoring to detect unusual and suspicious activity in the environment.

SUMMARY

A computer-implemented method, computer system, and computer program product for heat-based control of a surveillance system is provided. In an aspect, the method may include detecting a first pattern of heat transferred from a heat source based on a first sensor dataset corresponding to a first event, determining an expected pattern of heat to be transferred from the heat source during a second event based on the first pattern of heat transfer, and generating a surveillance model based on the expected pattern of heat transfer. The method may further include detecting a second pattern of heat transferred from the heat source based on a second sensor dataset corresponding to the second event, detecting the heat source during the second event with respect to the second pattern of heat transfer, and determining a threat level corresponding to a security risk posed by the heat source with respect to the environment.

DETAILED DESCRIPTION

Detailed embodiments of the present invention are disclosed herein for purposes of describing and illustrating claimed structures and methods that may be embodied in various forms, and are not intended to be exhaustive in any way, or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosed embodiments. The terminology used herein was chosen to best explain the principles of the one or more embodiments, practical applications, or technical improvements over current technologies, or to enable those of ordinary skill in the art to understand the embodiments disclosed herein. As described, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the embodiments of the present invention.

References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” or the like, indicate that the embodiment described may include one or more particular features, structures, or characteristics, but it shall be understood that such particular features, structures, or characteristics may or may not be common to each and every disclosed embodiment of the present invention herein. Moreover, such phrases do not necessarily refer to any one particular embodiment per se. As such, when one or more particular features, structures, or characteristics is described in connection with an embodiment, it is submitted that it is within the knowledge of those skilled in the art to affect such one or more features, structures, or characteristics in connection with other embodiments, where applicable, whether or not explicitly described.

Video surveillance produces large amounts of surveillance data that often cannot be effectively monitored 24/7 by human operators. In one respect, active monitoring of a surveillance feed by a human operator is insufficient in real-time applications requiring immediate detection of events, such as time-sensitive events including security breaches and the like. In another, surveillance reliability may be subject to random variation such as caused by lack of operator presence for monitoring a feed to detect and respond to suspicious, undesired, or anomalous activities and events.

To assist in monitoring surveillance feeds, some surveillance systems may implement motion detection sensors in capturing and recording surveillance data based on detected motion. In particular, a surveillance system may implement data generated by a motion detection sensor to activate and automatically control camera operation (e.g. focusing, zooming, panning, etc.), such as to monitor an environment with respect to transiently occurring events corresponding to the generated data. For example, when motion is detected, such as by external sensor or video motion detection, a corresponding signal (e.g. surveillance data) may be generated and delivered to an automated monitoring system to trigger video recording, control camera operation, and if necessary, generate and communicate an alert to the proper authorities.

However, in complex surveillance systems that include numerous cameras for monitoring an area or environment, where a wide variety of possible activities and events may occur, the incorporation of motion detection alone may be insufficient for controlling camera operation to effectively monitor an environment, and may cause high false alarm rates due to varying environmental or contextual conditions.

Accordingly, there is a need in the art for a method of surveillance control in an automated surveillance system that enables effective monitoring of an environment and accurate event detection with an acceptable false alarm rate.

Embodiments of the present invention are directed to a method of adaptive surveillance control in an automated surveillance system for targeted, context-based event detection in a monitored environment. In an aspect, the present invention may control camera operation based on thermal image data to monitor and detect events of interest in the environment according to user-defined preferences and conditions. The method may implement a machine learning technique to adaptively detect the events as such may vary in occurrence with respect to contextual conditions in the environment over time. The method may implement a machine learning process to monitor the environment and detect the events according to varying conditions over time.

Advantageously, the present invention overcomes the aforementioned problems of the prior art by implementing a combination of sensor modalities, along with machine learning, in controlling camera operation to monitor and detect events and activities in an environment. In particular, the present invention achieves a greater degree of situational and contextual awareness of the environment by implementing infrared sensors and cameras to control camera operation (i.e. of one or more cameras operating in the visual-range) based on the occurrence of spatio-temporal heat transfer patterns in the environment. The camera operation may be controlled so as to track the detected heat transfer patterns in the environment. The machine learning may be implemented in adapting the control of the camera operation to monitor and detect changing and evolving events and activities in the environment. To that end, the present invention improves the effectiveness of automated surveillance systems by reducing false alarm rates, as the determination of what does and does not constitute an event or activity of interest in the monitored environment may be continuously adapted to remain relevant to changing conditions and parameters in the environment. Further, by implementing infrared sensors and cameras the present invention increases the accuracy and sensitivity of environment monitoring and event detection by automated surveillance systems by enabling monitoring and detection of events based on a wider range conditions and parameters, thereby increasing the accuracy of detected events and the reliability of the automated surveillance systems. Other advantages will become readily apparent to those of skill in the art based on the descriptions of the present invention herein.

For purposes of the present disclosure, a “monitored environment” refers to any environment or area surveilled by an image capture device (e.g. a surveillance camera) for the purpose of security.

Referring now toFIG. 1, a functional block diagram of surveillance control system100is depicted, in accordance with an embodiment of the present invention. Surveillance control system100may include user device110, sensor node120, and surveillance control device130interconnected over network102. Surveillance control system100may further include an image capture device (not depicted), which may be used in a surveillance system to monitor an environment. While surveillance control system100is depicted inFIG. 1as including three discrete devices, other arrangements may be contemplated. For example, surveillance control system100may include a plurality of user devices such as user device110, and/or a plurality of sensor devices such as sensor node120(e.g. as in a sensor array). In various embodiments, user device110, sensor node120, and/or surveillance control device130may be formed by one or more integrated or distinct devices.

In various embodiments, network102may include an intranet, a local area network (LAN), a personal area network (PAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), a wireless mesh network, a wide area network (WAN) such as the Internet, or the like. Network102may include wired, wireless, or fiber optic connections. Network102may otherwise include any combination of connections and protocols for supporting communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

In various embodiments, user device110, sensor node120, and/or surveillance control device130may include a computing platform or node such as a microcontroller, a microprocessor, a wearable device, an implantable device, a mobile or smart phone, a tablet computer, a laptop computer, a desktop computer, a server such as a database server, a virtual machine, or the like. User device110, sensor node120, and/or surveillance control device130may otherwise include any other type of computing platform, computer system, or information system capable of sending and receiving data to and from another device, such as by way of network102. In certain embodiments, user device110, sensor node120, and/or surveillance control device130may include internal and external hardware components, as described with reference toFIG. 3. In other embodiments, user device110, sensor node120, and/or surveillance control device130may be implemented by way of a cloud computing environment, as described with reference toFIGS. 4 and 5.

User device110may host data communication module112. User device110may implement a combination of devices and technologies such as network devices and device drivers to support the operation of data communication module112, and to provide a platform enabling communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

Data communication module112may include an application or program such as a software program, one or more subroutines contained in a program, an application programming interface, or the like, to support communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

In an embodiment, user device110may receive user inputs from a user to generate user specification data for communication by data communication module112to surveillance control device130over network102, for monitoring the monitored environment, accordingly. In the embodiment, the user specification data may correspond to one or more user-defined parameters including user preferences related to event monitoring rules, event detection rules, event response rules, and the like. In the embodiment, the user specification data may define environmental and contextual conditions with respect to patterns of heat transfer by which occurrences of events in the monitored environment may be detected, monitored, and responded to, accordingly.

Sensor node120may host data communication module122. Sensor node120may implement a combination of devices and technologies such as network devices and device drivers, radio communication devices and control circuitry, or the like, to support the operation of data communication module122, and to provide a platform enabling communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

Data communication module122may include electronic circuitry, an application or program such as a software program, one or more subroutines contained in a program, an application programming interface, or the like, to support communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

In an embodiment, sensor node120may include a thermal sensor. The thermal sensor may include, for example, a thermal imaging device such as an infrared (IR) camera, a thermal imaging camera, thermographic camera, and the like. Sensor node120may otherwise include any type of heat-sensing sensor capable of detecting and measuring heat transferred to, from, or within the monitored environment such as by thermal conduction, convection, radiation, and/or transfer of energy by phase change. The heat transfer may include, for example, that caused by an exothermic process, mass flow, volume flow, electrical energy, and the like.

In an embodiment, sensor node120may be positioned in or about the monitored environment to detect signals corresponding to heat transferred to, from, or within the monitored environment. In the embodiment, surveillance control system100may include a sensor array including one or more sensors such as sensor node120, each positioned in or about the monitored environment to detect the signals from various perspectives with respect to the monitored environment.

In an embodiment, sensor node120may generate a sensor dataset for communication by data communication module122to surveillance control device130over network102, for monitoring of the monitored environment, accordingly. In the embodiment, the sensor dataset may include signal data corresponding to signals detected by sensor node120in the monitored environment during an associated time period, as well as sensor identifier data for associating a position of sensor node120with a position in the monitored environment from which the signals of the generated sensor dataset may be detected. In the embodiment, the sensor dataset may include signal data generated by one or more sensors of the sensor array.

Surveillance control device130may host surveillance control program140. Surveillance control device130may implement a combination of devices and technologies such as network devices and device drivers to support the operation of surveillance control program140, and to provide a platform enabling communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

Surveillance control program140may include data communication module142, pattern recognition module144, event management and response module146, training module148, and data storage150. Surveillance control program140may include an application or program such as a software program, one or more subroutines contained in a program, an application programming interface, or the like, to support communications between user device110, sensor node120, and surveillance control device130, in accordance with embodiments of the present invention.

Data communication module142may retrieve sensor datasets from the sensor array, receive user specification data from user device110, and communicate event response actions. Data communication module142may store the data in data storage150for retrieval and use by surveillance control program140, in accordance with embodiments of the present invention.

Pattern recognition module144may detect patterns of heat transferred from a heat source with respect to positions of the heat source in the monitored environment during a first event, and further, determine expected patterns of heat to be transferred from the heat source with respect to expected positions of the heat source in the monitored environment during a second, subsequent event. The heat may be transferred from the heat source to a region or object in the monitored environment.

In an embodiment, the heat source may include, for example, a living body or creature such as an animal (e.g. human, rodent, etc.), an exothermic process (e.g. chemical reaction, fire, etc.), fluid flow (e.g. combustion gases), energized electrical equipment, and the like.

For example, the heat source may include a mobile or stationary heated mass or hot body such as a warm-blooded living body, including a rodent, person, or the like, a particular region or portion of the heated mass such as a facial region of a person, or the like. As another example, the heat source may include a hot body such as a heated volume of fluid (i.e. hot air, combustion gas, liquid, etc.). The heat source may otherwise include any heated or hot body, system, or region or part thereof from which heat may be transferred to the monitored environment, such as to a surface of the monitored environment, an object in the monitored environment, a region or volume encompassing the heat source in the monitored environment, and so on, in accordance with embodiments of the present invention. That is, the heat source may include one of a pair of systems from which heat may be transferred to the other of the pair of systems, such as may occur as a result of a temperature difference or gradient between the pair of systems.

For purposes of the present disclosure, “hot body,” “heated mass,” and the like refers to any body existing in the monitored environment at a temperature above an ambient temperature of a region of the monitored environment.

In an embodiment, an event may include an occurrence or period in which an environmental or contextual condition in the monitored environment corresponds to a user-defined parameter. In the embodiment, the condition may correspond to a characteristic of the heat source with respect to the monitored environment, and may include, for example, a heat transfer rate, a pattern movement rate, a pattern growth rate, a heat or temperature gradient, a temperature of an object (e.g. the heat source), and the like. The condition may otherwise include any abnormal or anomalous indication corresponding to a potential security threat or hazard with respect to the heat source in the monitored environment, in accordance with embodiments of the present invention.

Event management and response module146may generate a surveillance model based on an expected pattern of heat transfer, determine a threat level corresponding to a security risk posed by the heat source with respect to the monitored environment, determine whether the threat level exceeds a predetermined threshold, and perform an event response action. Event management and response module146may further determine heat transfer characteristics of the heat source based on the detected patterns of heat, as previously described.

In an embodiment, the surveillance model may include executable program instructions corresponding to rules for performing event response actions. An event response action may be performed according to one or more event monitoring rules, event detection rules, and/or event response rules to monitor, detect, and/or respond to events in the monitored environment with respect to the heat source, accordingly. In the embodiment, the rules may include, for example, program instructions for execution by an image capture device in monitoring the heat source during an event, program instructions for execution in controlling one or more light sources in the monitored environment with respect to the heat source during the event, program instructions for execution in triggering an alarm in response to an occurrence of the event, and the like.

For example, the alert may relate to the presence of a rodent in the monitored environment and may be communicated to user device110and/or a pest management authority. As another example, the alert may relate to the presence of an intruder in the monitored environment and may be communicated to user device110and/or a security provider, a security response system, and the like.

Training module148may update the surveillance model to update the rules for performing the event response actions over time. In an embodiment, a machine learning algorithm such as a multi-layer neural network may be implemented to update the surveillance model. In the embodiment, the multi-layer neural network may include a three-layer architecture having adjustable neural weights between layers of neurons. For example, the neural network may include a logistic regression-based model having regularization functionality. The surveillance model may otherwise be updated using any other technique to enable data-driven monitoring and detection of heat transfer patterns in the monitored environment, in accordance with embodiments of the present invention.

Referring now toFIG. 2A, a flowchart depicting operational steps of an aspect of surveillance control system100is depicted, in accordance with an embodiment of the present invention.

At Step S202, pattern recognition module144may detect a first pattern of heat transferred from the heat source during a first event.

In an embodiment, the first pattern of heat may be detected based on a first sensor dataset corresponding to an occurrence of a first event in the monitored environment, which may correspond to heat transferred from the heat source to the monitored environment over a period of time during the first event. For example, the first pattern of heat corresponding to an occurrence of a first event may be detected in association with a pattern of heat transferred from the heat source (e.g. a person), as well as with a pattern of movement of the heat source (e.g. movements of the person) in a region in the monitored environment. The first sensor dataset may correspond to signals detected by the sensor array during the first event. In the embodiment, the first pattern of heat may be detected with respect to a movement speed and heat spread rate in a region of the monitored environment, based on the identifiers and corresponding positions of each sensor node120by which the first sensor dataset may be generated. In the embodiment, a heat flux at the region of the monitored environment may be determined based on the movement speed and heat spread rate at the region encompassing the heat source in the monitored environment.

In an embodiment, the first pattern of heat transfer may be detected by implementing a background subtraction method with respect to a heat map corresponding to the first sensor dataset. For example, pattern recognition module144may implement a background subtraction scheme that takes a Bayesian approach to model thermal responses of pixels of an image of the monitored environment as a mixture of Gaussians with unknown number of components. The background subtraction method may include one that takes into consideration the special characteristics of thermal imagery. Pattern recognition module144may otherwise implement any other method to detect regions of interest in the monitored environment, in accordance with embodiments of the present invention. In the embodiment, detecting the first pattern of heat transfer may include determining a shape of the heat transfer pattern.

In an embodiment, a heat map may be generated based on the detected first pattern of heat, with respect to the movement speed and heat spread rate, the heat flux, and the like, at the region encompassing the heat source in the monitored environment. In the embodiment, the heat map may correspond to a pattern of heat transferred from a facial region of a person over a particular time period.

At Step S204, event management and response module146may determine an expected pattern of heat to be transferred from the heat source during a second event based on the first pattern of heat transfer.

In an embodiment, the expected pattern of heat may be determined with respect to expected positions of the heat source in the monitored environment during the second event, based on the heat transfer rate (e.g. heat flux), the pattern movement rate, the pattern growth rate, the heat or temperature gradient, and/or the temperature of the heat source during the first event. In the embodiment, the expected pattern of heat may be used to determine a corresponding expected heat movement path of the heat source during the second event.

At Step S206, event management and response module146may generate a surveillance model based on the expected pattern of heat during the second event.

In an embodiment, the surveillance model may be generated based on the user specification data with respect to one or more user-defined parameters (i.e. user preferences) related to event monitoring rules, event detection rules, event response rules in the monitored environment. For example, the user may suffer from a rodent problem at home that may persist while the user is away (e.g. on vacation). While the rodent problem may not pose a security risk, the system may control the cameras in order to capture what and where the rodent is as well as to notify the owner or company that is supporting the owner for such rodent. Further, the user-defined parameters may indicate “allowed” heat sources in the monitored environment to be ignored, such as people and pets in the monitored environment, or heat sources with heat transfer rates below a user-specified threshold.

In an embodiment, the surveillance model may be generated by determining a heat transfer characteristic of the heat source based on the first and second sensor datasets and the second pattern of heat transfer. In the embodiment, the expected pattern of heat transfer may be classified with respect to a class of heat transfer patterns based on the heat emission characteristic, where the class of heat transfer patterns includes one or more modeled heat transfer patterns corresponding to a recurring pattern of heat transferred to the surveillance environment. In the embodiment, the surveillance model may be generated with respect to the class of heat emission patterns.

In an embodiment, the surveillance model may be updated by determining a significant heat transfer mode (i.e. conductive, convective, radiative, phase change) of the observed pattern of heat transfer. In the embodiment, the determined heat transfer mode may be classified with respect to a class of security threats.

At Step S208, pattern recognition module144may detect a second pattern of heat transferred from the heat source. The second pattern may correspond to an observed pattern of heat transferred from the heat source during the second event.

In an embodiment, the second pattern of heat may be detected based on a second sensor dataset corresponding to an occurrence of a second, subsequent event in the monitored environment, which may correspond to heat transferred from the heat source to the monitored environment over a period of time during the second event. The second sensor dataset may correspond to signals detected by the sensor array during the second event, such as in the same manner as that detected during the first event, as previously described with reference to Step S202. In the embodiment, the second pattern of heat may be detected in the same manner as the first pattern of heat, as described with reference to Step S202.

At Step S210, event management and response module146may monitor or continuously detect the heat source during the second event with respect to the second pattern of heat transfer.

In an embodiment, the heat source may be monitored by execution of the program instructions of the surveillance model (generated at Step S206) by an image capture device in the monitored environment with respect to the second pattern of heat transfer (detected at Step S208), to track, or to otherwise locate, position, and focus the heat source within a field-of-view of the device. In the embodiment, the program instructions may additionally or alternatively be executed to control energization of a light source to illuminate a region of the monitored environment with respect to the field-of-view of the device and/or the positions of the heat source in the monitored environment. The instructions for performing the actions may otherwise include instructions for increasing visualization clarity of the heat source as monitored by the image capture device, in accordance with embodiments of the present invention. The field-of-view of the device may include, for example, the extent of the observable world that is “seen” by the device at any given moment, and may depend on the solid angle through which the device is sensitive to electromagnetic radiation.

At Step S212, event management and response module146may determine a threat level corresponding to a security risk posed by the heat source with respect to the monitored environment.

In an embodiment, the threat level and the security risk may be determined based on a deviation between the expected pattern and the second (i.e. observed) pattern of heat transfer during the second event, with respect to differences in the static and dynamic features of the expected and second patterns of heat transfer.

In an embodiment, the threat level and the security risk may be determined based on a determination of mental state of the heat source. In the embodiment, the mental state may be determined based on the deviation between the expected and second patterns of heat transfer. For example, the change in mental state may correspond to a change in heat emitted from the heat source (e.g. of a person). In the embodiment, a comparison of the changes may be made based on corresponding heat maps of the detect patterns of heat transfer.

At Step S214, event management and response module146may determine that the threat level exceeds a predetermined threshold. If the threat level does not exceed the predetermined threshold, the method proceeds to Step S210. If the threat level exceeds the predetermined threshold, the method proceeds to Step S216. The predetermined threshold may be chosen as a matter of design, in accordance with embodiments of the present invention.

At Step S216, event management and response module146may perform an event response action, as previously described with reference to event management and response module146in the description ofFIG. 1. In an embodiment, the event response action may be performed based on the threat level corresponding security risk as determined at Step S212.

Referring now toFIG. 2B, a flowchart depicting operational steps of an aspect of surveillance control system100is depicted, in accordance with an embodiment of the present invention.

At step S218, training module148may retrieve a training dataset including historical behavioral characteristics data corresponding to behavioral characteristics of historical occupants of the monitored environment.

In an embodiment, the training dataset may include data corresponding to the monitored heat source (at Step S210). For example, the training dataset may include data corresponding to mood and cognitive state of the historical occupants, times of occupation by the historical occupants in the monitored environment, schedules and calendar activities associated with the historical occupants, detected conversations, geo-spatial metrics, and heat transfer patterns. The conversations may be detected by implementing a natural language processing (NLP) technique.

At step S220, training module148may determine a historical behavioral predisposition of each historical occupant based on the historical behavioral characteristics data.

In an embodiment, the machine learning algorithm may be implemented to determine the historical behavioral predisposition of each historical occupant. For example, the multi-layer neural network may include a three-layer architecture having adjustable neural weights between layers of neurons that may cluster the historical behavioral predispositions of each historical occupant based on cognitive state. Feedback may be generated in conjunction with re-configurable neural weights assigned for assignment to the neural weights with respect to the user specification data.

At step S222, training module148may determine a behavioral predisposition of the heat source by classification with respect to the historical behavioral predisposition of each historical occupant.

In an embodiment, the behavioral predisposition of the heat source may be determined with a level of confidence by classification with respect to the clustered historical behavioral predispositions of each historical occupant (at Step S220). For example, the real-time interaction and engagement patterns and sequences of the heat source may be classified with respect to the clustered historical occupants by analyzing a plurality of historical occupant activities such as conversations and control of certain objects in the monitored environment.

At step S224, training module148may generate surveillance model update data based on the classification.

In an embodiment, the machine learning algorithm may be executed based on sensor datasets corresponding to respective events in the monitored environment. In the embodiment, the update may include updated program instructions for updating the surveillance model (generated at Step S206). The updates may be applied to remove the false positives and true negatives that would otherwise be detected by way of the image capture devices in the monitored environment based on the detected patterns of heat.

As an implementation example, an algorithmic approach may include the following steps: 1) For each User Ui in the current detected list of users U, Get Ui characteristics: tone t, personality p, language expression 1, facial gestures g, body gesture/action b, Ui(t, p, l, g, b); 2) analyze Ui (t, p, l, g, b) to determine cognitive state and behavior Ui(cs, be); 3) if Ui(cs, be) surpass an initial threshold time t_w, a monitoring session is started for Ui and cohorts; 4) for each history record Ui_Hi in Ui_H, If Ui_Hi contains an old user cognitive state behavior Ui(cs, be) that triggered a feedback to the system that is similar to the current Ui(cs, be), then a cluster monitoring session is started for Ui and cohorts; 5) Continuously monitor Ui and cohorts. Get Ui characteristics: tone t, personality p, language expression 1, facial gestures f, body gesture/action b, Ui(t, p, l, g, b); 6) change configuration on components; 7) measure Ui reactions to change based on changes in heat transfer patterns; 8) determine, for each clustering action P_Ai, whether each contains a set of machine comprehensible actions, a duration, a prioritization, and a set of user cognitive states and behaviors Ux(cs, be) for which the P_Ai is recommended; 9) set the order of clustering action in which to minimize effect on usage; 10) select P_Ai according to the current Ui(cs, be) and priority P_Ai_p; 11) after P_Ai execution duration, monitoring session continues; 12) if Ui(cs, be) is below warning threshold for a configured amount of time, the monitoring session is finished; and 13) save Ui_H for future reference.

FIG. 3is a block diagram depicting user device110, sensor node120, and/or surveillance control device130, in accordance with an embodiment of the present invention.

As depicted inFIG. 3, user device110, sensor node120, and/or surveillance control device130may include one or more processors902, one or more computer-readable RAMs904, one or more computer-readable ROMs906, one or more computer readable storage media908, device drivers912, read/write drive or interface914, network adapter or interface916, all interconnected over a communications fabric918. The network adapter916communicates with a network930. Communications fabric918may be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system.

One or more operating systems910, and one or more application programs911, such as surveillance control program140residing on surveillance control device130, as depicted inFIG. 1, are stored on one or more of the computer readable storage media908for execution by one or more of the processors902via one or more of the respective RAMs904(which typically include cache memory). In the illustrated embodiment, each of the computer readable storage media908may be a magnetic disk storage device of an internal hard drive, CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk, a semiconductor storage device such as RAM, ROM, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

User device110, sensor node120, and/or surveillance control device130may also include a R/W drive or interface914to read from and write to one or more portable computer readable storage media926. Application programs911on user device110, sensor node120, and/or surveillance control device130may be stored on one or more of the portable computer readable storage media926, read via the respective R/W drive or interface914and loaded into the respective computer readable storage media908. User device110, sensor node120, and/or surveillance control device130may also include a network adapter or interface916, such as a Transmission Control Protocol (TCP)/Internet Protocol (IP) adapter card or wireless communication adapter (such as a 4G wireless communication adapter using Orthogonal Frequency Division Multiple Access (OFDMA) technology). Application programs911may be downloaded to the computing device from an external computer or external storage device via a network (for example, the Internet, a local area network or other wide area network or wireless network) and network adapter or interface916. From the network adapter or interface916, the programs may be loaded onto computer readable storage media908. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. User device110, sensor node120, and/or surveillance control device130may also include a display screen920, a keyboard or keypad922, and a computer mouse or touchpad924. Device drivers912interface to display screen920for imaging, to keyboard or keypad922, to computer mouse or touchpad924, and/or to display screen920for pressure sensing of alphanumeric character entry and user selections. The device drivers912, R/W drive or interface914and network adapter or interface916may include hardware and software (stored on computer readable storage media908and/or ROM906).

User device110, sensor node120, and/or surveillance control device130can be a standalone network server, or represent functionality integrated into one or more network systems. In general, user device110, sensor node120, and/or surveillance control device130can be a laptop computer, desktop computer, specialized computer server, or any other computer system known in the art. In certain embodiments, user device110, sensor node120, and/or surveillance control device130represents computer systems utilizing clustered computers and components to act as a single pool of seamless resources when accessed through a network, such as a LAN, WAN, or a combination of the two. This implementation may be preferred for data centers and for cloud computing applications. In general, user device110, sensor node120, and/or surveillance control device130can be any programmable electronic device, or can be any combination of such devices.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and heat-based surveillance controlling96. Heat-based surveillance controlling96may include functionality enabling the cloud computing environment to perform heat-based surveillance control, in accordance with embodiments of the present invention.

While the invention has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the appended claims and their equivalents. Therefore, the present invention has been disclosed by way of example for purposes of illustration, and not limitation.