Methods and systems for automated suspect identification in retail establishments

Abstract

Systems and methods are disclosed for identifying a suspect in an activity at an establishment, including obtaining, from a video of the activity, an image identifying the suspect, and identifying the suspect as a criminal in the video of the activity or other videos of activities.

Description

039636.07719 METHODS AND SYSTEMS FOR AUTOMATED SUSPECT IDENTIFICATION IN RETAIL ESTABLISHMENTS CLAIM OF PRIORITY

[0001] The present Application for Patent claims priority to U.S. Provisional Patent Application No. 63 / 532,822, entitled “METHODS AND SYSTEMS FOR AUTOMATED SUSPECT IDENTIFICATION IN RETAIL ESTABLISHMENTS” filed August 15, 2023, which is assigned to the assignee hereof and hereby expressly incorporated by reference herein in its entirety for all purposes. BACKGROUND

[0002] The present disclosure relates to monitoring retail locations, and more particularly to identifying suspects in criminal behavior occurring in a retail establishment.

[0003] Retailers can have security systems deployed within retail establishments for detecting various activities within the retail establishments, such as movement of goods, movement or actions of consumers, etc. A security system may include one or more cameras or other sensors for recording consumer activity. The cameras can record video that can be viewed or reviewed by security personnel to detect theft or potentially suspicious behavior.

[0004] In some cases, however, the security personnel may not be able to apprehend theft in a suitable time or before the suspect exits the retail establishment. In addition, security personnel may review videos after crimes occur in the retail establishment, but may not be able to identify suspects committing the crimes. SUMMARY

[0005] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the DETAILED DESCRIPTION. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] In an aspect, a method of identifying a suspect in an activity at an establishment is providing that includes obtaining, from a video of the activity, an image identifying the suspect, and identifying the suspect as a criminal in the video of the activity or other videos of activities, including sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal, and039636.07719 receiving, from the criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

[0007] In another aspect, an apparatus for identifying a suspect in an activity at an establishment is provided that includes one or more memories, and one or more processors coupled to the one or more memories. The one or more processors are configured to execute instructions stored on the one or more memories to obtain, from a video of the activity, an image identifying the suspect, and identify the suspect as a criminal in the video of the activity or other videos of activities, including sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal, and receiving, from the criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

[0008] In another aspect, one or more computer-readable media storing instructions, executable by one or more processors, for identifying a suspect in an activity at an establishment are provided. The instructions includes instructions for obtaining, from a video of the activity, an image identifying the suspect, and identifying the suspect as a criminal in the video of the activity or other videos of activities, including, sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal, and receiving, from the criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

[0009] In another aspect, a security system is provided that includes various hardware, software, or other components for identifying a suspect in an activity at an establishment using one or more methods described herein. In another aspects, a security system is provided that includes means for identifying a suspect in an activity at an establishment using one or more methods described herein. In another aspect, a computer-readable medium is provided herein that stores computer executable instructions for identifying a suspect in an activity at an establishment using one or more methods described herein.

[0010] Further aspects of the present disclosure are described in more details below.039636.07719 BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The disclosed aspects will hereinafter be described in conjunction with the appended drawings, provided to illustrate and not to limit the disclosed aspects, wherein like designations denote like elements, and in which:

[0012] FIG. 1 is a block diagram of an example of a security system, according to implementations of the present disclosure;

[0013] FIG. 2 is a block diagram of an example of a control system implemented by the security system of FIG.1, according to implementations of the present disclosure;

[0014] FIG.3 is a flowchart of an example of a method implemented by the security system of FIG.1, according to implementations of the present disclosure; and

[0015] FIG. 4 is a block diagram of examples components of a computer device that may implement one or more of the features of the security system of FIG.1. DETAILED DESCRIPTION

[0016] The detailed description set forth below in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In some instances, well known components may be shown in block diagram form in order to avoid obscuring such concepts.

[0017] In many retail establishments, there is a need of identifying suspects participating in criminal activity in the retail establishment, such as theft or other crimes. Retail establishments typically include security systems with cameras for capturing video of consumer behavior throughout the retail establishment, but the systems may not be able to effectively identify the suspects.

[0018] The present disclosure addresses one or more shortcomings of security systems for retail establishments by automatedly engaging a criminal database provider to assist in identifying suspects involved in criminal activity at the retail establishment. For example, a security system in the retail establishment can detect criminal activity occurring in the retail establishment, such as based on an indication from security personnel of the criminal activity in captured video, based on computer vision models039636.07719 detecting the criminal activity in captured video, etc. Based at least in part on detecting the criminal activity, one or more images of a suspect in the captured video can be sent to the criminal database provider for identifying the suspect. The criminal database provider can compare the one or more images of the suspect to other stored images of suspects to identify the suspect, and can return identification of the suspect, if located, to the security system. For example, the identification can include a name, social security number, address, or other identifying information. In addition, for example, the criminal database provider can return an indication of criminal history of the suspect to the security system of the retail establishment.

[0019] Based on receiving the identification of the suspect, the security system can take further action, such as providing the identity of the suspect, the criminal history, the captured video, and / or other captured video related to or including the same identified suspect, to a law enforcement system. In other examples, based on receiving the identification of the suspect (or otherwise identifying the suspect in subsequent captured video), the security system can prevent access to portions of the retail establishment for the suspect, such as access to frictionless point-of-sale (POS) systems, locked merchandise cabinets, restrooms or other areas of the retail establishment, accessing the front door if the suspect subsequently arrives at the retail establishment, etc. In addition, for example, the security system can continue to collect video with the same individual, and can wait until a number of activities are captured, an amount of goods are stolen, etc. before engaging the criminal database provider to identify the suspect. In an example, this can allow the retail establishment to collect sufficient evidence of criminal activity for the given suspect before contacting law enforcement. In any case, automated suspect identification in the captured video can allow the retail establishment to obtain a case file of activities by the suspect, automatically report the suspect and case file to law enforcement, block the suspect from accessing parts of the retail establishment, etc.

[0020] Turning now to the figures, example aspects are depicted with reference to one or more modules or components described herein, where modules or components in dashed lines may be optional.

[0021] Referring to FIG.1, an example of a security system 100 deployed at an establishment 102 (e.g., a retail establishment, such as a store) is depicted. The security system 100 may include, for example, one or more cameras 104 installed within (or outside of) the establishment 102. For example, the one or more cameras 104 can be fixed039636.07719 cameras attached to a structure within the establishment 102, such as a ceiling or wall, attached to another structure in the parking lot, etc., and / or can be attached to a drone or other unmanned aerial system (UAS), robot, or other device that can move throughout the establishment 102. In yet another example, the one or more cameras 104 can be part of a mobile device (e.g., a cellular phone) carried by an employee of the establishment, or worn by the employee (e.g., an outward facing camera on a body of the employee). In any case, the one or more cameras 104 can be communicatively coupled with the control system 120 via a wired or wireless communication medium to provide images or videos 106 to the control system 120. The camera(s) 104 can capture video of an individual 116 moving throughout the establishment, where the captured video may facilitate detection of an activity being performed by the individual 116, an identification of the individual 116 in previous captured videos, etc.

[0022] The control system 120 can utilize an artificial intelligence (AI) module 122, via a network 124 or a locally executing AI module, to detect certain activity in captured video, such as potential theft or other crimes, to identify an individual in the captured video as being the same as an individual from previously captured video, etc. In an example, AI module 122 can use a ML model, such as a neural network of images, videos, or other inputs, to compare captured images, videos, or other inputs to detect the criminal activity or associated individuals. For example, the ML model can be trained with images, videos, etc. indicated as associated with certain activities that are desired to be detected, such as theft or crimes, and the ML model can accordingly receive images, videos, etc. as input, and provide an output as to whether the input images, videos, etc. include any of the activities on which the ML model is trained, provide a confidence score as to whether the input images, videos, etc. include the activities, or other output from which a determination can be made as to whether the input images include (or likely include) the activities of interest.

[0023] In one example, AI module 122 can use a generative adversarial network (GAN), which can include a ML model in which two neural networks (a generator and a discriminator) that compete with each other by using deep learning methods to become more accurate in their predictions. In other examples, AI module 122 can bypass the GAN (e.g., for a different neural network used), or can bypass one of the generator or discriminator (e.g., assuming they are well trained) in detecting potential theft or other suspicious activity based on provided inputs.039636.07719

[0024] The control system 120 can also communicate, e.g., via network 124, with a criminal database provider 126 for identifying a suspect in captured video and / or a law enforcement system 128 for reporting criminal activity and suspect identification. For example, the network 124 can include the Internet and / or multiple nodes between the control system 120, an enterprise network to which the control system 120 is connected, etc. and the criminal database provider 126 and / or law enforcement system 128.

[0025] In an example, the criminal database provider 126 can include a node provided by a government entity (e.g., a county or city government entity, a state government entity, a federal government entity, etc.) that includes a database storing information regarding criminals, including crimes committed by the criminals and associated information, identifying information of the criminals (e.g., name, social security number, last known address or phone number, distinguishing features, etc.). In an example, the criminal database provider 126 may publicly provide records of the criminals or the establishment may be granted access to the criminal database provider 126 (e.g., and / or issued credentials for accessing the criminal database provider 126) by the government entity. In another example, the criminal database provider 126 may be shared by a collection of establishments (e.g., owned by the same entity or otherwise) that tracks criminal activity within the establishment. In this example, the criminal database provider 126 can similarly include a node having a database storing the information regarding the criminals, which can be accessed by the control system 120.

[0026] In addition, in an example, the law enforcement system 128 may include a node provided by a law enforcement agency (e.g., a county or city law enforcement agency, such as a police force, a state or federal law enforcement agency, such as a bureau of investigation, etc.) that includes a mechanism for reporting potential criminal activity. For example, the law enforcement system 128 may include an interface (e.g., application programming interface (API)) that the control system 120 can use to report potential criminal activity. For example, the law enforcement system 128 may publicly expose the interface or may provide the enterprise with credentials for accessing the law enforcement system 128, etc. to provide information on potential crimes being committed at the establishment, such as video captured via cameras 104 that is detected (e.g., by AI module 122) as including (or likely including) criminal activity, an identification of an individual 116 in the video (e.g., obtained from the039636.07719 criminal database provider 126), other data related to the detected incident, such as time, location, etc., and / or the like.

[0027] In accordance with aspects described herein, the control system 120 can detect criminal activity occurring in images / video 106 captured via the cameras 104, and / or other inputs from other sensors. For example, control system 120 can provide captured video 106 to AI module 122 as input and can receive, from the AI module 122, an indication of whether the captured video 106 includes criminal activity and / or whether the captured video 106 includes a same individual that is in previously captured video. If the captured video includes criminal activity, for example, control system 120 can provide at least a portion of the capture video to criminal database provider 126 to obtain a possible identification of a suspect in the captured video, a criminal history of the suspect, etc. Where the suspect is identified, control system 120 can control one or more features of the establishment 102, as described, can provide the identification and / or criminal history to law enforcement system 128 along with the captured video, etc.

[0028] Referring to FIG.2, details of the control system 120 for identifying suspects detected as engaging in criminal activities in a retail establishment are illustrated. In an example, the control system 120 may include an image processing module 202 for capturing and / or sending a video and / or images from one or more cameras to analyze for detecting certain activities or identifying suspect(s) performing the activities, or an identification receiving module 204 for receiving an identification of at least one suspect in the captured video. In an example, the control system 120 may optionally include a suspect reporting module 206 for reporting the suspect and / or associated criminal information or the captured video to a law enforcement system, an access controlling module 208 for controlling access to one or more features at the retail establishment, and / or a suspect tracking module 210 for tracking movement or a journey of the suspect throughout the establishment.

[0029] For example, the various modules 202, 204, 206, 208, 210 of the control system 120 can be provided or implemented by one or more processors, one or more memories, instructions stored on the one or more memories and executable by the one or more processors, etc., of the control system 120, as described further herein. In addition, control system 120 can include a communications module for communicating with one or more other devices, such as the one or more cameras 104 to receive the images or video, communicating with the AI module 122 to provide the video or item039636.07719 information input to the AI module 122 and / or receive an output therefrom, communicating with the criminal database provider 126 or the law enforcement system 128, etc., as described in various examples herein. In some examples, the AI module 122 may be part of (e.g., provided by) the control system 120, such that the control system 120 may store the ML model and / or interact with the ML model to provide image, video, etc. input and receive an output indicating whether criminal activities are detected.

[0030] Referring to FIG.3, an example of a method 300 of identifying suspects engaging in criminal activity in a retail establishment is depicted. The operations of the method 300 may be performed by one or more modules or components of the security system 100, control system 120, etc., as described herein.

[0031] At 302, the method 300 may include obtaining, from a video of an activity, an image including or identifying a suspect. For example, image processing module 202 of the control system 120 can obtain, from the video of the activity, the image including or identifying the suspect. In an example, the image processing module 202 can obtain the video of the activity based on detecting that the activity in the video is likely criminal activity, such as theft, violence, aggressive behavior, vandalism, or other crimes. For example, image processing module 202 can provide the captured video to the AI module 122, which can use computer vision analytics (e.g., in conjunction with an ML model trained with videos of criminal activities for which detection is desired) to detect whether the captured video includes activity that is likely criminal activity. For example, the AI module 122 can receive the captured video as input, and can compare the captured video to other videos that have criminal activity, using the ML model, to determine whether the captured video has criminal activity, a confidence or probability score that the captured video has criminal activity, etc. Image processing module 202 can accordingly determine that the captured video includes criminal activity. In any case, image processing module 202 can obtain, from the captured video, an image of the suspect, which can be obtained or detected using object detection to discern or detect the object / individual present within the video and performing the potentially criminal activity.

[0032] At 304, the method 300 can include identifying the suspect as a criminal in the video of the activity or other videos of activities. For example, image processing module 202 can identify the suspect as a criminal in the video of the activity and / or in other videos of activities. In one example, image processing module 202 can identify the039636.07719 criminal activity using AI module 122, as described above, and can identify the suspect (e.g., the object / individual in the video or an associated image) as a criminal based on identifying the criminal activity. In another example, image processing module 202 can process the image with other images or videos of the same individual in previously performed criminal activities to detect the individual as the same individual in the images or videos of previously performed criminal activities, detect a pattern of behavior or activities by the individual resulting in identifying the individual as a criminal, etc. In an example, where the suspect is identified as a criminal (or assumed or detected, by image processing module 202, to be engaged in criminal activity based on actions detected from the image and / or video), AI module 122 can store the image and / or provide the image to the control system 120 for subsequently identifying the individual as a suspect in subsequent video captures, such as by using facial recognition or other facial capture, feature extraction, and / or feature comparison technologies.

[0033] In another example, identifying the suspect as a criminal at 304 can include, at 306, sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal. For example, image processing module 202 can send the image to the criminal database provider 126 (e.g., via network 124). For example, the criminal database provider 126 can determine whether the suspect is a known criminal, which can include comparing the image of the suspect to one or more images stored in an image database in an attempt to identify the suspect (e.g., using facial recognition or other facial capture, feature extraction, and / or feature comparison technologies). If the suspect is identified, the criminal database provider 126 can provide, to the control system 120, an identification of the suspect (e.g., name, address, social security number, etc.), a criminal history associated with the suspect, and / or the like.

[0034] In another example, identifying the suspect as a criminal at 304 can include, at 308, receiving, from the criminal database provider, an identification of the suspect as a known criminal or receiving a criminal history associated with the suspect. For example, identification receiving module 204 can receive, from the criminal database provider, the identification of the suspect as a known criminal or receive the criminal history associated with the suspect. In this regard, for example, the criminal database provide 126 can provide an interface (e.g., API) for the control system 120 to send039636.07719 suspect images as input and receive a suspect identification and / or criminal history as output.

[0035] At 310, method 300 can optionally include sending, based on identifying the suspect as a known criminal, the video of the activity to a law enforcement system along with the identification or the criminal history of the suspect. For example, suspect reporting module 206 can send, based on identifying the suspect as a known criminal, the video of the activity to the law enforcement system 128 along with the identification or the criminal history of the suspect. For example, suspect reporting module 206 can send the video, identification, criminal history, etc. to the law enforcement system 128 using an interface provided by the law enforcement system 128 to receive the information, as described. In an example, this can cause law enforcement to open a case and / or pursue the suspect with criminal charges for the criminal activity in the video. In an example, suspect reporting module 206 can wait until additional activities are detected with the same individual before sending to law enforcement to build a stronger case against the individual. Thus, for example, suspect reporting module 206 can store incidents and / or related video or other information determined to involve the suspect (e.g., based on suspect identification as described above), and wait until it has a certain number of activities, an amount of stolen goods, etc., before sending the video (or videos) of the activity (or activities) to the law enforcement system 128.

[0036] At 312, the method 300 can optionally include denying, based on identifying the suspect as a criminal, access to one or more features of the establishment. For example, access controlling module 208 can deny, based on identifying the suspect as a criminal, access to one or more features of the establishment. For example, access controlling module 208 can communicate with automated access devices deployed at the establishment, such as frictionless POS systems, locked cabinets, restrooms, building entrances, or other parts of the establishment, etc., and can accordingly control access to such devices based on identifying the suspect as a criminal. For example, access controlling module 208 can detect presence of the suspect based on video capture, device identification, etc. as being within the establishment or within a threshold proximity of the one or more features, and can accordingly lock the one or more features from use.

[0037] At 314, the method 300 can optionally include detecting the activity occurring at the establishment. As described, for example, image processing module 202 can detect039636.07719 the activity occurring based on images or videos captured at the one or more cameras 104, analyzing the images or videos using AI module 122 or otherwise to detect a type of activity, an individual identified in the captured videos, etc. In an example, the ML model can be trained using videos of different types of activities, and AI module 122 can detect a type of activity occurring in the input images or video based on an output from the ML model, such as an indication of a type of activity, a confidence score that the type of activity is occurring in the images or video, etc.

[0038] At 316, the method 300 can optionally include tracking, via one or more cameras and based on detecting the activity, a journey of the suspect throughout the establishment. For example, suspect tracking module 210 can track, via the one or more cameras and based on detecting the activity, the journey of the suspect throughout the establishment using one or more movement tracking technologies. For example, when the activity is detected (e.g., as criminal activity), suspect tracking module 210 can control the one or more cameras to track the suspect, such as by controlling pan / tilt / zoom (PTZ) cameras to move and focus on the suspect as the suspect travels throughout the establishment. In another example, suspect tracking module 210 can control a drone or robot to follow the suspect, etc.

[0039] At 318, the method 300 can optionally include storing, in a case file that includes at least one other activity and associated journey of the suspect, a video of the journey as captured by the one or more cameras. For example, suspect tracking module 210 can store the video of the journey as captured by the one or more camera in the case file (e.g., in memory) that can include at least one other activity and associated journey of the suspect. The case file can be used for further study to determine if criminal activity occurred, suspect identification, etc., and / or can be provided to the law enforcement system 128, as described. For example, suspect tracking module 210 can associate the journeys in the case file based on the identification of the suspect by the criminal database provider 126 (e.g., using facial recognition or other facial capture, feature extraction, and / or feature comparison technologies) or based on control system 120 determining that the suspects in the videos are the same (e.g., based on computer vision modeling, as described).

[0040] At 320, the method 300 can optionally include tracking a video of an automobile entered by the suspect, and obtaining, from the video of the automobile, a license plate number, and storing the license plate number in the case file. For example, as part of the journey or based on detecting that the suspect has exited the establishment, suspect039636.07719 tracking module 210 can track, via the one or more cameras 104, the suspect leaving the establishment 102 (e.g., which may include tracking the suspect setting of an exit alarm with stolen merchandise) and entering an automobile, and suspect tracking module 210 can capture the license plate number of the automobile. Suspect tracking module 210 can store the license plate number in the case file, which can be provided to the law enforcement system 128, as described above.

[0041] Referring to FIG. 4, a computing device 400 may implement all or a portion of the functionality described in FIGS.1-3. For example, the computing device 400 may be or may include at least a portion of the control system 120, or any other module or component described herein with reference to FIGS.1-3. The computing device 400 may include one or more processors 402 which may be configured to execute or implement software, hardware, and / or firmware modules that perform some or all of the functionality described herein with reference to FIGS. 1-3. For example, the processor(s) 402 may be configured to execute or implement software, hardware, and / or firmware modules that perform some or all of the functionality described herein with reference to the control system 120, or any other module or component described herein with reference to FIGS.1-3.

[0042] The processor(s) 402 may be a micro-controller, an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA), and / or may include a single or multiple set of processors or multi-core processors. Moreover, the processor(s) 402 may be implemented as an integrated processing system and / or a distributed processing system. The computing device 400 may further include memory / memories 404, such as for storing local versions of applications being executed by the processor(s) 402, related instructions, parameters, etc. The memory / memories 404 may include a type of memory usable by a computer, such as random access memory (RAM), read only memory (ROM), tapes, magnetic discs, optical discs, volatile memory, non-volatile memory, and any combination thereof. Additionally, the processor(s) 402 and the memory / memories 404 may include and execute an operating system executing on the processor(s) 402, one or more applications, display drivers, etc., and / or other modules or components of the computing device 400.

[0043] Further, the computing device 400 may include a communications module 406 that provides for establishing and maintaining communications with one or more other devices, parties, entities, etc. utilizing hardware, software, and services. The039636.07719 communications module 406 may carry communications between modules on the computing device 400, as well as between the computing device 400 and external devices, such as devices located across a communications network and / or devices serially or locally connected to the computing device 400. In an aspect, for example, the communications module 406 may include one or more buses, and may further include transmit chain modules and receive chain modules associated with a wireless or wired transmitter and receiver, respectively, operable for interfacing with external devices.

[0044] Additionally, the computing device 400 may include a data store 408, which can be any suitable combination of hardware and / or software, that provides for mass storage of information, databases, and programs. For example, the data store 408 may be or may include a data repository for applications and / or related parameters not currently being executed by processor(s) 402. In addition, the data store 408 may be a data repository for an operating system, application, display driver, etc., executing on the processor 402, and / or one or more other modules of the computing device 400.

[0045] The computing device 400 may also include a user interface module 410 operable to receive inputs from a user of the computing device 400 and further operable to generate outputs for presentation to the user (e.g., via a display interface to a display device). The user interface module 410 may include one or more input devices, including but not limited to a keyboard, a number pad, a mouse, a touch-sensitive display, a navigation key, a function key, a microphone, a voice recognition module, or any other mechanism capable of receiving an input from a user, or any combination thereof. Further, the user interface module 410 may include one or more output devices, including but not limited to a display interface, a speaker, a haptic feedback mechanism, a printer, any other mechanism capable of presenting an output to a user, or any combination thereof.

[0046] Additionally, aspects of the present disclosure may be implemented according to one or any combination of the following clauses.

[0047] Clause 1. A method for identifying a suspect in an activity at an establishment, comprising: obtaining, from a video of the activity, an image identifying the suspect; and identifying the suspect as a criminal in the video of the activity or other videos of activities, including: sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal; and receiving, from the039636.07719 criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

[0048] Clause 2. The method of clause 1, further comprising sending, based on identifying the suspect as a known criminal, the video of the activity to a law enforcement system along with the identification or the criminal history of the suspect.

[0049] Clause 3. The method of any of the preceding clauses, wherein sending the image to the criminal database provider is based on identifying the suspect in a threshold number of activities at the establishment.

[0050] Clause 4. The method of any of the preceding clauses, further comprising: detecting the activity occurring at the establishment; tracking, via one or more cameras and based on detecting the activity, a journey of the suspect throughout the establishment; and storing, in a case file that includes at least one other activity and associated journey of the suspect, a video of the journey of the suspect as captured by the one or more cameras.

[0051] Clause 5. The method of any of the preceding clauses, wherein sending the video to the law enforcement system includes sending the case file to the law enforcement system.

[0052] Clause 6. The method of any of the preceding clauses, wherein tracking the journey includes tracking an exit alarm set off by the suspect, or tracking a video of an automobile entered by the suspect.

[0053] Clause 7. The method of any of the preceding clauses, further comprising obtaining, from the video of the automobile, an indication of a license plate number of the automobile, wherein sending the case file to the law enforcement system includes sending the license plate number to the law enforcement system.

[0054] Clause 8. The method of any of the preceding clauses, further comprising denying, based on identifying the suspect as a criminal, access to one or more features of the establishment.

[0055] Clause 9. An apparatus for identifying a suspect in an activity at an establishment, comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to execute instructions stored on the one or more memories to: obtain, from a video of the activity, an image identifying the suspect; and identify the suspect as a criminal in the video of the activity or other videos of activities, including: sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal; and receiving, from the039636.07719 criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

[0056] Clause 10. The apparatus of clause 9, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send, based on identifying the suspect as a known criminal, the video of the activity to a law enforcement system along with the identification or the criminal history of the suspect.

[0057] Clause 11. The apparatus of any of the preceding clauses, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send the image to the criminal database provider based on identifying the suspect in a threshold number of activities at the establishment.

[0058] Clause 12. The apparatus of any of the preceding clauses, wherein the one or more processors are configured to execute instructions stored on the one or more memories to: detect the activity occurring at the establishment; track, via one or more cameras and based on detecting the activity, a journey of the suspect throughout the establishment; and store, in a case file that includes at least one other activity and associated journey of the suspect, a video of the journey of the suspect as captured by the one or more cameras.

[0059] Clause 13. The apparatus of any of the preceding clauses, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send the video to the law enforcement system including sending the case file to the law enforcement system.

[0060] Clause 14. The apparatus of any of the preceding clauses, wherein the one or more processors are configured to execute instructions stored on the one or more memories to track the journey including tracking an exit alarm set off by the suspect, or tracking a video of an automobile entered by the suspect.

[0061] Clause 15. The apparatus of any of the preceding clauses, wherein the one or more processors are configured to execute instructions stored on the one or more memories to obtain, from the video of the automobile, an indication of a license plate number of the automobile, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send the case file to the law enforcement system including sending the license plate number to the law enforcement system.039636.07719

[0062] Clause 16. The apparatus of any of the preceding clauses, wherein the one or more processors are configured to execute instructions stored on the one or more memories to deny, based on identifying the suspect as a criminal, access to one or more features of the establishment.

[0063] Clause 17. One or more computer-readable media storing instructions, executable by one or more processors, for identifying a suspect in an activity at an establishment, the instructions comprising instructions for: obtaining, from a video of the activity, an image identifying the suspect; and identifying the suspect as a criminal in the video of the activity or other videos of activities, including performing the method of any of clauses 1 to 8.

[0064] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” include any combination of A, B, and / or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,”039636.07719 “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”

[0065] As used in this application, the terms “component,” “module,” “system” and the like are intended to include a computer-related entity, such as but not limited to hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and / or thread of execution and a component can be localized on one computer and / or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate by way of local and / or remote processes such as in accordance with a signal having one or more data packets, such as data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems by way of the signal.

[0066] As used herein, a processor, at least one processor, and / or one or more processors, individually or in combination, configured to perform or operable for performing a plurality of actions is meant to include at least two different processors able to perform different, overlapping or non-overlapping subsets of the plurality actions, or a single processor able to perform all of the plurality of actions. In one non-limiting example of multiple processors being able to perform different ones of the plurality of actions in combination, a description of a processor, at least one processor, and / or one or more processors configured or operable to perform actions X, Y, and Z may include at least a first processor configured or operable to perform a first subset of X, Y, and Z (e.g., to perform X) and at least a second processor configured or operable to perform a second subset of X, Y, and Z (e.g., to perform Y and Z). Alternatively, a first processor, a second processor, and a third processor may be respectively configured or operable to perform a respective one of actions X, Y, and Z. It should be understood that any combination of one or more processors each may be configured or operable to perform any one or any combination of a plurality of actions.039636.07719

[0067] As used herein, a memory, at least one memory, and / or one or more memories, individually or in combination, configured to store or having stored thereon instructions executable by one or more processors for performing a plurality of actions is meant to include at least two different memories able to store different, overlapping or non-overlapping subsets of the instructions for performing different, overlapping or non-overlapping subsets of the plurality actions, or a single memory able to store the instructions for performing all of the plurality of actions. In one non-limiting example of one or more memories, individually or in combination, being able to store different subsets of the instructions for performing different ones of the plurality of actions, a description of a memory, at least one memory, and / or one or more memories configured or operable to store or having stored thereon instructions for performing actions X, Y, and Z may include at least a first memory configured or operable to store or having stored thereon a first subset of instructions for performing a first subset of X, Y, and Z (e.g., instructions to perform X) and at least a second memory configured or operable to store or having stored thereon a second subset of instructions for performing a second subset of X, Y, and Z (e.g., instructions to perform Y and Z). Alternatively, a first memory, and second memory, and a third memory may be respectively configured to store or have stored thereon a respective one of a first subset of instructions for performing X, a second subset of instruction for performing Y, and a third subset of instructions for performing Z. It should be understood that any combination of one or more memories each may be configured or operable to store or have stored thereon any one or any combination of instructions executable by one or more processors to perform any one or any combination of a plurality of actions. Moreover, one or more processors may each be coupled to at least one of the one or more memories and configured or operable to execute the instructions to perform the plurality of actions. For instance, in the above non-limiting example of the different subset of instructions for performing actions X, Y, and Z, a first processor may be coupled to a first memory storing instructions for performing action X, and at least a second processor may be coupled to at least a second memory storing instructions for performing actions Y and Z, and the first processor and the second processor may, in combination, execute the respective subset of instructions to accomplish performing actions X, Y, and Z. Alternatively, three processors may access one of three different memories each storing one of instructions for performing X, Y, or Z, and the three processor may in combination execute the respective subset039636.07719 of instruction to accomplish performing actions X, Y, and Z. Alternatively, a single processor may execute the instructions stored on a single memory, or distributed across multiple memories, to accomplish performing actions X, Y, and Z.

Claims

039636.07719 CLAIMS WHAT IS CLAIMED IS:

1. A method for identifying a suspect in an activity at an establishment, comprising: obtaining, from a video of the activity, an image identifying the suspect; and identifying the suspect as a criminal in the video of the activity or other videos of activities, including: sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal; and receiving, from the criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

2. The method of claim 1, further comprising sending, based on identifying the suspect as a known criminal, the video of the activity to a law enforcement system along with the identification or the criminal history of the suspect.

3. The method of any of claims 1 or 2, wherein sending the image to the criminal database provider is based on identifying the suspect in a threshold number of activities at the establishment.

4. The method of any of claims 1 to 3, further comprising: detecting the activity occurring at the establishment; tracking, via one or more cameras and based on detecting the activity, a journey of the suspect throughout the establishment; and storing, in a case file that includes at least one other activity and associated journey of the suspect, a video of the journey of the suspect as captured by the one or more cameras.

5. The method of any of claims 1 to 4, wherein sending the video to the law enforcement system includes sending the case file to the law enforcement system.039636.07719 6. The method of any of claims 1 to 5, wherein tracking the journey includes tracking an exit alarm set off by the suspect, or tracking a video of an automobile entered by the suspect.

7. The method of any of claims 1 to 6, further comprising obtaining, from the video of the automobile, an indication of a license plate number of the automobile, wherein sending the case file to the law enforcement system includes sending the license plate number to the law enforcement system.

8. The method of any of claims 1 to 7, further comprising denying, based on identifying the suspect as a criminal, access to one or more features of the establishment.

9. An apparatus for identifying a suspect in an activity at an establishment, comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to execute instructions stored on the one or more memories to: obtain, from a video of the activity, an image identifying the suspect; and identify the suspect as a criminal in the video of the activity or other videos of activities, including: sending the image identifying the suspect to a criminal database provider to identify whether the suspect is a known criminal; and receiving, from the criminal database provider, an identification of the suspect as a known criminal or a criminal history associated with the suspect.

10. The apparatus of claim 9, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send, based on identifying the suspect as a known criminal, the video of the activity to a law enforcement system along with the identification or the criminal history of the suspect.

11. The apparatus of any of claims 9 or 10, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send the039636.07719 image to the criminal database provider based on identifying the suspect in a threshold number of activities at the establishment.

12. The apparatus of any of claims 9 to 11, wherein the one or more processors are configured to execute instructions stored on the one or more memories to: detect the activity occurring at the establishment; track, via one or more cameras and based on detecting the activity, a journey of the suspect throughout the establishment; and store, in a case file that includes at least one other activity and associated journey of the suspect, a video of the journey of the suspect as captured by the one or more cameras.

13. The apparatus of claims 9 to 12, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send the video to the law enforcement system including sending the case file to the law enforcement system.

14. The apparatus of claims 9 to 13, wherein the one or more processors are configured to execute instructions stored on the one or more memories to track the journey including tracking an exit alarm set off by the suspect, or tracking a video of an automobile entered by the suspect.

15. The apparatus of claims 9 to 14, wherein the one or more processors are configured to execute instructions stored on the one or more memories to obtain, from the video of the automobile, an indication of a license plate number of the automobile, wherein the one or more processors are configured to execute instructions stored on the one or more memories to send the case file to the law enforcement system including sending the license plate number to the law enforcement system.

16. The apparatus of claims 9 to 15, wherein the one or more processors are configured to execute instructions stored on the one or more memories to deny, based on identifying the suspect as a criminal, access to one or more features of the establishment.039636.07719 17. One or more computer-readable media storing instructions, executable by one or more processors, for identifying a suspect in an activity at an establishment, the instructions comprising instructions for performing the method of any of claims 1 to 8.

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