Systems, methods, and computer program products for entry-related security determinations

Abstract

The present invention provides systems, methods, and computer program products for entry-related security determinations. An exemplary method includes: receiving first image data of a field of view of a first imaging device, the first image data including an entry location; and determining an entry condition for the entry location based on the first image data. In response to a first user attempting to enter the entry location, the method includes receiving second image data of a field of view of a second imaging device, the second image data including the first user for whom a security determination is to be performed. The method also includes: generating a security parameter for the first user indicating the presence and location of a security device for the first user; comparing the security parameter to a verification threshold; and generating an alarm signal in a case where the security parameter fails to meet the verification threshold.

Description

Technical Field

[0001] Exemplary embodiments of the present disclosure relate generally to access control systems and, more particularly, to regulating access control to ensure proper security authentication. Background Art

[0002] In many environments such as manufacturing facilities, warehouses, etc., workers (e.g., employees, contractors, staff, etc.) may be exposed to various harmful conditions as part of performing their related duties in these environments. These conditions may cause injuries to workers without the proper use of safety equipment (e.g., personal protective equipment (PPE)) and compliance with relevant safety protocols. In addition, workers in some environments (such as hospitals or medical facilities) may face an increased risk of exposure to bacteria, viruses, or infectious diseases. In order to prevent or reduce this risk, PPE may be recommended or required, such as according to applicable industry standards, government regulations, etc. The inventors have identified many deficiencies in the prior art in the art, and the remedies for these deficiencies are the subject of the embodiments described herein. Summary of the invention

[0003] As described above, many industries and environments are associated with various conditions that may be harmful to employees, contractors, employees, etc. working in these environments. By way of example, as part of normal operation, some industrial environments may produce sounds that are harmful to the ears of workers, and / or produce dust, suspended particles, corrosive chemicals, flying objects, etc. that are potentially harmful to the eyes or respiratory system of workers if inhaled or ingested. Therefore, many industry regulations require workers to use safety equipment such as earplugs, safety glasses / goggles, masks or other PPE to reduce or eliminate the possibility of such damage. In addition, the increasing risk of transmission of infectious diseases, bacteria, viruses, etc. has led to the development of various government regulations regarding the wearing of PPE such as masks to prevent or reduce the risks associated with these diseases.

[0004] Conventional systems that attempt to determine the correct use of safety equipment or PPE may rely on various exemplary image processing techniques. For example, such systems may analyze multiple two-dimensional (2D) images to determine whether a particular user is wearing a particular PPE item. However, these image processing techniques are typically computationally intensive and require increased processing resources and / or increased processing time in order for safety equipment or PPE to be determined. Therefore, these conventional systems are not suitable for various specific implementations that require real-time or substantially real-time determination. By way of example, building entrances, turnstiles, corridors, checkpoints, gates, and / or other entry locations often have a large number of users (e.g., high-traffic environments) attempting to enter or leave a particular location. Due to the amount of image processing calculations or determinations associated with these high-traffic environments, many conventional systems may not be able to perform verification determinations of safety equipment or PPE in a timely manner, resulting in allowing some such users to enter, and these users fail to include or correctly locate one or more safety devices associated with a particular location. In addition, in some low-traffic environments, these conventional systems may waste computing resources due to performing image processing of the following images: images that fail to include user requests to enter (e.g., images in which there is no user, or images in which the user does not attempt to enter a particular location).

[0005] To address these and other issues, exemplary implementations of embodiments of the present disclosure may utilize multiple imaging devices and machine learning techniques (e.g., artificial neural networks, convolutional neural networks, etc.) to provide security compliance determinations in response to entry requests in near real time. In operation, embodiments of the present disclosure may generate image data associated with a first imaging device that captures an entry location, such as a bar code located on or near an entry door. In the event that an entry condition indicates that a user is attempting to enter the entry location, second image data from a second imaging device may be used to determine security parameters indicating the presence and location of a security device for the user. Comparisons between security parameters and associated verification thresholds may be used to quickly and reliably determine the presence and location of a security device. In addition, the use of multiple imaging devices may be used to gate or buffer any required image processing determinations, thereby reducing the burden placed on these computing resources.

[0006] Apparatus, methods, systems, devices, and associated computer program products are provided for entry-related security determinations. An exemplary method may include: receiving first image data of a field of view of a first imaging device, the first image data including an entry location; and determining an entry condition for the entry location based on the first image data. In a case where the entry condition indicates that a first user is attempting to enter the entry location, the method may include receiving second image data of a field of view of a second imaging device, the second image data including the first user for whom a security determination is to be performed. The method may also include generating a security parameter associated with the first user within the field of view of the second imaging device, the security parameter indicating the presence and location of a security device for the first user. The method may also include comparing the security parameter to a verification threshold, and generating an alarm signal in a case where the security parameter fails to meet the verification threshold.

[0007] In some embodiments, generating the alarm signal may further include generating a notification for display, the notification including the safety parameter.

[0008] In some embodiments, generating the alarm signal may further include preventing the first user from entering the entry location.

[0009] In some embodiments, generating an alarm signal may also include changing an entry condition of the entry location.

[0010] In some embodiments, determining an entry condition for the entry location may further include: identifying an instance of a scannable indicia in the first image data; and determining an open-state entry condition for the entry location based on the scannable indicia.

[0011] In other embodiments, determining the entry condition of the entry location may further include: identifying the absence of a scannable indicia in the first image data; and determining the open-state entry condition of the entry location based on the absence of the scannable indicia.

[0012] In some embodiments, generating the security parameters may also include comparing second image data including the first user to calibrated image data associated with the first user.

[0013] In other embodiments, generating the security parameters may further include providing the second image data to the machine learning model.

[0014] The above-mentioned summary of the invention is provided only for the purpose of summarizing some exemplary embodiments, so as to provide a basic understanding of some aspects of the present disclosure. Therefore, it should be understood that the above-mentioned embodiments are only examples and should not be construed as narrowing the scope or essence of the present disclosure in any way. It should be understood that in addition to those summarized here, the scope of the present disclosure also covers many possible embodiments, some of which will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Having generally described certain exemplary embodiments of the present disclosure above, reference will now be made to the accompanying drawings. In certain embodiments described herein, the components shown in the accompanying drawings may or may not exist. Some embodiments may include fewer (or more) components than those shown in the drawings.

[0016] Figure 1 An exemplary system including an imaging device for entry-related security determinations according to some example embodiments described herein is shown;

[0017] Figure 2 shows a schematic block diagram of exemplary circuits that can perform various operations according to some exemplary embodiments described herein;

[0018] Figure 3 shows an exemplary flow chart of entry-related security determinations according to some exemplary embodiments described herein;

[0019] Figure 4 shows an exemplary flow chart of entry condition determination according to some exemplary embodiments described herein; and

[0020] Figure 5 An exemplary flow chart for security parameter generation according to some exemplary embodiments described herein is shown. DETAILED DESCRIPTION

[0021] Some embodiments of the present disclosure will be described more fully below with reference to the accompanying drawings, in which some embodiments of the present disclosure are shown, but not all embodiments are shown. In fact, the present disclosure can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; on the contrary, these embodiments are provided so that the present disclosure meets applicable legal requirements. Throughout the text, similar reference numerals refer to similar elements. As used herein, this specification may refer to the computing device of the exemplary system as an exemplary "device". However, the elements of the device described herein may be equally applicable to the methods and computer program products protected by the claims. Therefore, the use of any such terms should not be understood as limiting the spirit and scope of the embodiments of the present disclosure.

[0022] Definition of terms

[0023] As used herein, the terms "data", "content", "information", "electronic information", "signal", "command" and similar terms are used interchangeably to refer to data that can be sent, received and / or stored according to embodiments of the present invention. Therefore, the use of any such terms should not be understood to limit the essence or scope of the embodiments of the present disclosure. In addition, where a first device is described herein as receiving data from a second device, it should be understood that the data can be received directly from the second device or can be received indirectly via one or more intermediate computing devices (such as, for example, one or more servers, repeaters, routers, network access points, base stations, hosts, etc., sometimes referred to as "networks" herein). Similarly, where a first device is described herein as sending data to a second device, it should be understood that the data can be sent directly to the second device or can be sent indirectly via one or more intermediate computing devices (such as, for example, one or more servers, remote servers, cloud-based servers (e.g., cloud tools), repeaters, routers, network access points, base stations, hosts, etc.).

[0024] As used herein, the term "comprising" means including but not limited to, and should be interpreted in the manner commonly used in the patent context. It should be understood that the use of broad terms such as "comprising," "including," and "having" provides support for narrower terms such as "consisting of," "consisting essentially of," and "consisting essentially of."

[0025] As used herein, the phrases "in one embodiment," "according to one embodiment," "in some embodiments," and the like generally mean that the particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure. Thus, the particular feature, structure, or characteristic may be included in more than one embodiment of the present disclosure, such that these phrases are not necessarily referring to the same embodiment.

[0026] As used herein, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.

[0027] As used herein, the term "first imaging device" or "first imager" refers to one or more devices capable of generating first image data. Exemplary first imaging devices may include a 2D camera, a three-dimensional (3D) camera, a stereo camera, a depth camera, a time-of-flight (TOF) camera or sensor, a rangefinder camera, a barcode reader or scanner, a scanning device, a quick response (QR) code scanner or camera, a direct part mark (DPM) scanner or camera, etc., which can generate image data indicating the field of view (FOV) of the first imaging device. In other words, the first imaging device of the present disclosure may include any device configured to generate first image data, as described below, which first image data includes an entry position. As a non-limiting example, the first imaging device may include a barcode scanner, reader, or camera (e.g., a camera of a mobile device or other device) that is configured to identify scannable marks (e.g., barcodes, etc.) within the FOV of the first imaging device. Although described below with reference to an exemplary first imaging device that generates first image data for identifying an entry location and associated conditions, the present disclosure also contemplates that other location detection technologies (e.g., RFID tags, geolocation data, proximity sensors, location sensors, etc.) may be used in place of or in addition to the first imaging device. In addition, although described below with reference to a barcode as an exemplary scannable indicia, the present disclosure also contemplates that one or more display devices (e.g., a liquid crystal display (LCD), a display of a mobile device, etc.) may be used additionally or alternatively to present a scannable indicia (e.g., a digital embodiment).

[0028] As used herein, the term "second imaging device" or "second imager" refers to one or more devices capable of generating second image data. Exemplary second imaging devices may also include 2D cameras, 3D cameras, stereo cameras, depth cameras, TOF cameras or sensors, rangefinder cameras, barcode readers or scanners, scanning devices, QR code scanners or cameras, DPM scanners or cameras (e.g., cameras of mobile devices or other devices), etc., which can generate image data indicating the FOV of the second imaging device. In other words, the second imaging device of the present disclosure may include any device configured to generate second image data, as described below, which includes the first user to be performed on the security determination. As a non-limiting example, the second imaging device may include a 2D or 3D camera configured to capture an image including the first user and any associated security devices of the first user within the FOV of the second imaging device. Although described below with reference to an exemplary second imaging device that generates second image data including the first user and the associated security devices, the present disclosure also contemplates that other sensors or equivalent technologies (e.g., RFID tags, geolocation data, proximity sensors, position sensors, etc.) can be used to replace or supplement the second imaging device.

[0029] As used herein, the term "computing device" refers to any user device, controller, object, or system that can physically or network communicate with a first imaging device and / or a second imaging device, as described below. For example, a computing device may refer to a wireless electronic device that is configured to perform various entry condition determination and / or security parameter related operations in response to first image data and / or second image data generated by a first imaging device and / or a second imaging device, respectively. The computing device may be configured to communicate with the first imaging device and / or the second imaging device via Bluetooth, NFC, Wi-Fi, 3G, 4G, 5G protocols, etc. In some cases, the computing device may include the first imaging device and / or the second imaging device (e.g., an integrated configuration).

[0030] As used herein, the terms "safety equipment" and "PPE" are used interchangeably to refer to clothing, clothing, equipment, pads, guards, shields, masks, etc. that are configured to reduce or prevent injury or infection to a user. Safety equipment and PPE may be further configured to prevent or mitigate physical, chemical, and / or biological damage to a user. For example, safety equipment and PPE may include hearing protection (e.g., earplugs, earmuffs, etc.), eye protection (e.g., goggles, face shields, glasses, etc.), skin protection (e.g., gloves, cut-resistant shields, protective hazardous materials, particle blocking items, etc.), respiratory protection (e.g., respirators, masks, etc.), and the like. In other words, the present disclosure contemplates that the safety equipment and PPE described herein may refer to any device, system, or element that at least partially protects a user from potential hazards.

[0031] As used herein, the term "entry location" may refer to a physical location where security equipment or PPE verification may occur. For example, an entry location may refer to a building entrance, a turnstile, a corridor, a checkpoint, a gate, an entrance, and / or other physical location where a user may request entry. Although the following is described with reference to a door that can be used as an entrance and / or exit for a first user as an exemplary entry location, the present disclosure also contemplates the intended application of an entry-related security determination system, and any location may be an entry location. As a specific example, an entry location may refer to an entry checkpoint, a gate system, or other entrance through which a user may be guided to enter a specific location (e.g., enter an office building, board an aircraft, etc.). In addition, an entry location may be associated with an "entry condition" indicating the state of the entry location and / or indicating the user's interaction with the entry location. By way of example, an entry condition for an exemplary door (e.g., an entry location) may indicate that the door is physically open, physically closed, partially open, open for use by some users, closed for use by some users, etc. In addition, an entry condition may indicate that the user is currently entering the entry location, has previously entered the entry location, and / or is requesting to enter the entry location. Although described herein with reference to exemplary entry locations that physically restrict the movement of a user (e.g., an entry door or other location), the present disclosure also contemplates that an entry location may also refer to any designated location where a user may make a PPE-related verification determination. For example, an entry location may refer to a section, segment, portion, etc. of an area where a person may be directed to await a PPE-related analysis (e.g., a roped-off section, a taped area of ​​a room, etc.).

[0032] As used herein, the term "computer-readable medium" refers to non-transitory storage hardware, non-transitory storage devices, or non-transitory computer system memory that can be accessed by a computing device, a microcomputing device, a computing system, or a module of a computing system to encode computer-executable instructions or software programs thereon. The non-transitory "computer-readable medium" can be accessed by a computing system or a module of a computing system to retrieve and / or execute computer-executable instructions or software programs encoded on the medium. Exemplary non-transitory computer-readable media may include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (e.g., one or more magnetic storage disks, one or more optical disks, one or more USB flash drives), computer system memory, or random access memory (such as DRAM, SRAM, EDO RAM), etc.

[0033] After listing a series of definitions required throughout this application, the following describes an exemplary system architecture and exemplary devices for implementing exemplary embodiments and features of the present disclosure.

[0034] Device Architecture and Exemplary Apparatus

[0035] refer to Figure 1 , the exemplary security system 100 is shown as having a first imaging device 102 and a second imaging device 105, which are operably coupled to the computing device 200 via a network 104. As defined above, the first imaging device 102 may include a device capable of generating first image data, and may include a 2D camera, a 3D camera, a stereo camera, a depth camera, a TOF camera or sensor, a range-finding camera, a barcode reader or scanner, a scanning device, a QR code scanner or camera, a DPM scanner or camera, etc. The first imaging device 102 may be associated with a FOV 103. The FOV 103 may refer to an observable area within which the first imaging device 102 can capture images (e.g., generate first image data). As described below, in some embodiments, the first imaging device 102 may be positioned or oriented so that the entry location 107 is physically located within the FOV 103 of the first imaging device 102. In other words, FOV 103 of first imaging device 102 may be such that first image data generated by first imaging device 102 (e.g., a captured image of FOV 103) may include image data indicative of or otherwise associated with entry location 107. The present disclosure contemplates that first imaging device 102 may be positioned at any physical location and in any orientation based on the intended application of system 100. Furthermore, the present disclosure contemplates that FOV 103 may vary based on operating parameters of first imaging device 102.

[0036] As defined above, the second imaging device 105 may also include a device capable of generating second image data, and may include a 2D camera, a 3D camera, a stereo camera, a depth camera, a TOF camera or sensor, a rangefinder camera, a barcode reader or scanner, a scanning device, a QR code scanner or camera, a DPM scanner or camera, etc. The second imaging device 105 may be associated with a FOV 106. The FOV 106 may refer to an observable area within which the second imaging device 105 may capture images (e.g., generate second image data). As described below, in some embodiments, the second imaging device 105 may be positioned or oriented so that one or more users (e.g., the first user 101) are at least partially located within the FOV 106 of the second imaging device 105. In other words, the FOV 106 of the second imaging device 105 may be such that the second image data generated by the second imaging device 105 (e.g., a captured image of the FOV 106) may include image data indicating or otherwise associated with the first user 101. The present disclosure contemplates that the second imaging device 105 may be positioned at any physical location and in any orientation based on the intended application of the system 100. Furthermore, the present disclosure contemplates that the FOV 106 may vary based on operating parameters of the second imaging device 105.

[0037] In some embodiments as described herein, the first imaging device 102 and the second imaging device 105 may be formed as an integrated device, or may be otherwise co-housed. In such embodiments, the FOV 103 and the FOV 105 may, for example, at least partially overlap. In other embodiments, the first imaging device 102 and the second imaging device 105 may be positioned within such a common housing so that the FOV 103 and the FOV 105 do not overlap. As a specific example, the first imaging device 102 may be oriented near the entry location 107 (e.g., an entry door), and the second imaging device 105 may be positioned in the opposite direction toward one or more users attempting to enter the entry location 107. In such examples, the FOV 103 and the FOV 106 may be in opposite directions (e.g., the first imaging device 102 facing backward and the second imaging device 105 facing forward). In other embodiments, the first imaging device 102 and the second imaging device 105 may be positioned separately. In such embodiments, FOV 103 and FOV 106 may be similarly adjusted based on the positioning of first imaging device 102 and second imaging device 105 based on the intended application of system 100. In any embodiments, the present disclosure contemplates that FOV 103 and / or FOV 106 may be dynamically adjusted (e.g., tilted, translated, pivoted, etc.) during performance of the operations described herein.

[0038] Alternatively or in addition, the first imaging device 102 and / or the second imaging device 105 may include additional or alternative technologies to include Bluetooth, RFID, biometrics, etc. Furthermore, in some examples, the first imaging device 102 and / or the second imaging device 105 may implement a proximity card system that may support HID Prox, EM4102, and AWID proximity technologies.

[0039] Continue to refer Figure 1, the security system 100 may include a computing device 200 that is connected to the first imaging device 102 and the second imaging device 105 via the network 104. In some cases, the first imaging device 102 may include the computing device 200 in whole or in part. In some cases, the second imaging device 105 may include the computing device 200 in whole or in part. In other cases, the first imaging device 102, the second imaging device 105, and the computing device 200 may be formed as a single integrated device. The computing device 200 may include circuits, networked processors, etc. configured to perform some or all of the device-based (e.g., based on entry-related security determinations) processes described herein, and may be any suitable processing device and / or network server. In this regard, the computing device 200 may be embodied by any of a variety of devices. For example, the computing device 200 may be configured to receive / send data (e.g., image data) and may include any of a variety of fixed terminals, such as a server, a desktop computer, or an information kiosk, or may include any of a variety of mobile terminals, such as a portable digital assistant (PDA), a mobile phone, a smart phone, a laptop computer, a tablet computer, or a peripheral device connected to one or more fixed or mobile terminals in some embodiments. The exemplary embodiments contemplated herein may have various form factors and designs, but will still include at least Figure 2 Components shown and described in conjunction with the figure. In some embodiments, computing device 200 may include several servers or computing devices that perform interconnected and / or distributed functions. Although many arrangements are contemplated herein, computing device 200 is shown and described herein as a single computing device to avoid unnecessarily complicating the present disclosure.

[0040] The network 104 may include one or more wired or wireless communication networks, including, for example, wired and / or wireless local area networks (LANs), personal area networks (PANs), metropolitan area networks (MANs), wide area networks (WANs), etc., and any hardware, software, and / or firmware (such as network routers, switches, hubs, etc.) for implementing the one or more networks. For example, the network 104 may include cellular phones, mobile broadband, long-term evolution (LTE), GSM / EDGE, UMTS / HSPA, IEEE802.11, IEEE 802.16, IEEE802.20, Wi-Fi, dial-up, and / or WiMAX networks. In addition, the network 104 may include a public network (such as the Internet), a private network (such as an intranet), or a combination thereof, and may utilize a variety of networking protocols currently available or later developed, including but not limited to TCP / IP-based networking protocols. In some embodiments, the network 104 may refer to a collection of wired connections, such that the first imaging device 102, the second imaging device 105, and / or the computing device 200 may be physically connected via one or more network cables, etc.

[0041] In some embodiments, the system 100 may include a calibration image database 110, which may be hosted by the computing device 200 or otherwise hosted by a device in communication with the computing device 200. The calibration image database 110 may be stored by any suitable storage device configured to store some or all of the information described herein (e.g., the memory 204 of the computing device 200 or a separate memory system separate from the computing device 200, such as one or more database systems provided by another device (e.g., an online application or a third-party provider), a back-end data server, a network database, a cloud storage device, etc.). The calibration image database 110 may include data received from the computing device 200 (e.g., via the memory 204 and / or the processor 202), and the corresponding storage device may therefore store the data. In some embodiments, the calibration image database 100 may be configured to store image data generated by one or more of the first imaging device 102 or the second imaging device 105. By way of example, as part of an initial calibration procedure, the calibration image database 110 may be configured to receive and store image data associated with multiple users. This image data (eg, calibrated image data) may be used for comparison with image data generated by, for example, the second imaging device 105 as part of security parameter determination, as described below.

[0042] like Figure 2 As shown, computing device 200 may include processor 202, memory 204, input / output circuit 206, and communication circuit 208. In addition, computing device 200 may include image processing circuit 210, machine learning circuit 212, and / or scanning circuit 214. Computing device 200 may be configured to perform the following combined Figures 3 to 5 operations described herein. Although in some cases functional language is used to describe components 202 to 214, it should be understood that a particular implementation necessarily includes the use of specific hardware. It should also be understood that certain of these components 202 to 214 may include similar or common hardware. For example, both sets of circuits may use the same processor 202, memory 204, communication circuits 208, etc. to perform their associated functions, such that each set of circuits does not require duplicate hardware. The use of the term "circuitry" as used herein includes specific hardware that is configured to perform the functions associated with the corresponding circuits described herein. As described in the above examples, in some embodiments, various elements or components of the circuitry of the computing device 200 may be housed within the first imaging device 102 and / or the second imaging device 105. In this regard, it should be understood that some of the components described in conjunction with the computing device 200 may be housed in Figure 1 The other components are contained in one or more of the devices, while the other components are contained in another of the devices, or by Figure 1 Yet another device not explicitly shown in the figure accommodates.

[0043] Of course, although the term "circuit" should be broadly understood to include hardware, in some embodiments, the term "circuit" may also include software for configuring hardware. For example, although a "circuit" may include processing circuits, storage media, network interfaces, input / output devices, etc., other elements of the computing device 200 may provide or supplement the functionality of a particular circuit.

[0044] In some embodiments, the processor 202 (and / or a coprocessor or any other processing circuitry assisting the processor or otherwise associated with the processor) may communicate with the memory 204 via a bus for transferring information between components of the computing device 200. The memory 204 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In other words, for example, the memory may be an electronic storage device (e.g., a non-transitory computer-readable storage medium). The memory 204 may be configured to store information, data, content, applications, instructions, etc. for enabling the computing device 200 to perform various functions according to the exemplary embodiments of the present disclosure.

[0045] The processor 202 may be embodied in a variety of different ways, and may, for example, include one or more processing devices configured to execute independently. Additionally or alternatively, the processor may include one or more processors configured in series via a bus to enable independent execution of instructions, pipelining, and / or multithreading. The use of the term "processing circuitry" may be understood to include a single-core processor, a multi-core processor, multiple processors within a computing device, and / or a remote or "cloud" processor.

[0046] In an exemplary embodiment, the processor 202 may be configured to execute instructions stored in the memory 204 or otherwise accessible to the processor 202. Alternatively or in addition, the processor 202 may be configured to perform hard-coded functions. Therefore, whether configured by hardware or by a combination of hardware and software, the processor 202 may represent an entity (e.g., physically embodied in circuit form) that is capable of performing operations according to embodiments of the present disclosure while being configured accordingly. Alternatively, for example, when the processor 202 is embodied as an executor of software instructions, these instructions may specifically configure the processor 202 to perform the algorithms and / or operations described herein when executing these instructions.

[0047] The computing device 200 also includes an input / output circuit 206, which in turn can communicate with the processor 202 to provide output to the user and receive input from the user, user device, or another source. In this regard, the input / output circuit 206 may include a display that can be manipulated by a mobile application. In some embodiments, the input / output circuit 206 may also include additional functions, including a keyboard, a mouse, a joystick, a touch screen, a touch area, soft keys, a microphone, a speaker, or other input / output mechanisms. The processor 202 and / or a user interface circuit including the processor 202 may be configured to control one or more functions of the display by computer program instructions (e.g., software and / or firmware) stored on a memory accessible to the processor (e.g., memory 204, etc.).

[0048] The communication circuitry 208 may be any means, such as a device or circuit embodied in hardware or a combination of hardware and software, that is configured to receive and / or transmit data from and / or to a network and / or any other device, circuit, or module that communicates with the computing device 200. In this regard, the communication circuitry 208 may include, for example, a network interface for implementing communications with a wired or wireless communication network. For example, the communication circuitry 208 may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware and / or software, or any other device suitable for implementing communications via a network. Additionally or alternatively, the communication interface may include circuitry for interacting with one or more antennas to cause signals to be transmitted via the one or more antennas or to process signals received via the one or more antennas. These signals may be sent by the computing device 200 using any of a variety of wireless personal area network (PAN) technologies, such as Version 1.0 to 3.0, Bluetooth Low Energy (BLE), infrared wireless (e.g., IrDA), ultra-wideband (UWB), inductive wireless transmission, etc. In addition, it should be understood that these signals can be sent using Wi-Fi, near field communication (NFC), Worldwide Interoperability for Microwave Access (WiMAX), or other proximity-based communication protocols.

[0049] The image processing circuit 210 includes a hardware component designed to generate a security parameter associated with the first user 101 within the FOV 106 of the second imaging device 105 based on the second image data. The security parameter generated by the image processing circuit 210 may indicate the presence and location of the security device of the first user 101. The image processing circuit 210 may utilize a processing circuit such as a processor 202 to perform its corresponding operations, and may utilize a memory 204 to store the collected information. In some cases, the image processing circuit 210 may also include a machine learning circuit 212, which includes a hardware component designed to utilize artificial intelligence to generate security parameters. By way of example, the machine learning circuit 212 may include or utilize an artificial neural network or a convolutional neural network trained on image data of at least a plurality of users. The machine learning circuit 212 may also utilize a processing circuit such as a processor 202 to perform its corresponding operations, and may utilize a memory 204 to store the collected information.

[0050] In some embodiments, the scanning circuit 214 includes a hardware component designed to determine the entry condition of the entry location 107. By way of example, in some cases, the entry location 107 may include a scannable indicia 109 (e.g., a barcode or other indicia) that can be captured by the first imaging device 102. The scanning circuit 214 can be configured to recognize the scannable indicia 109 and determine the entry condition of the entry location 107. The scanning circuit 214 can utilize processing circuitry (such as the processor 202) to perform its corresponding operations, and can utilize the memory 204 to store the collected information.

[0051] It should also be understood that in some embodiments, the image processing circuit 210, the machine learning circuit 212 and / or the scanning circuit 214 may include a separate processor, a specially configured field programmable gate array (FPGA) or an application specific interface circuit (ASIC) to perform their respective functions.

[0052] In addition, computer program instructions and / or other types of code may be loaded onto a computer, processor, or other programmable circuit to produce a machine, such that the computer, processor, or other programmable circuit that executes the code on the machine forms a device for implementing various functions, including those described in conjunction with the components of the computing device 200.

[0053] As described above and based on the present disclosure, it will be understood that the embodiments of the present disclosure may be configured as devices, systems, methods, etc. Therefore, the embodiments may include various devices, which include complete hardware or any combination of software and hardware. In addition, the embodiments may take the form of a computer program product, which includes instructions stored on at least one non-transitory computer-readable storage medium (e.g., computer software stored on a hardware device). Any suitable computer-readable storage medium may be utilized, including a non-transitory hard disk, a CD-ROM, a flash memory, an optical storage device, or a magnetic storage device.

[0054] Example Operations for Entering Related Security Determinations

[0055] Figure 3 A flow chart including a series of operations for entering into a relevant security determination is shown. As described above, Figure 3 The operations illustrated may be performed, for example, by, with the assistance of, and / or under the control of an apparatus (e.g., computing device 200). In this regard, performance of the operations may invoke one or more of processor 202, memory 204, input / output circuitry 206, communication circuitry 208, image processing circuitry 210, machine learning circuitry 212, and / or scanning circuitry 214.

[0056] As described below, Figure 3 The operation of may be used to determine: (1) whether the entry location 107 is currently open or closed; and (2) whether the first user 101 attempting to enter such entry location 107 is properly wearing one or more PPE items. By way of example, a camera (e.g., a first imaging device 102) may capture an image (e.g., first image data) associated with a FOV 103 of the camera (e.g., the first imaging device 102). The FOV 103 may include the entry location 107 as defined above, and may also include a barcode or other scannable indicia 109. The system may determine the current state or position (e.g., entry condition) of the entry location 107 by analyzing the image of the camera (e.g., the first imaging device 102). In response to the determination, another camera (e.g., a second imaging device 105) may capture an image (e.g., second image data) associated with a FOV 106 of the camera (e.g., the second imaging device 105). The FOV 106 may include one or more users (e.g., at least the first user 101) who may attempt to enter the entry location 107. As described below, in some cases, FOV 103 and FOV 106 may at least partially overlap.

[0057] The system may analyze the image (e.g., second image data) of the camera (e.g., second imaging device 105) to determine the presence and location (e.g., safety parameters) of any PPE items used by one or more users (e.g., at least first user 101). The presence and location of the PPE may be compared to known or other valid PPE usages to determine whether the PPE is worn correctly (e.g., compared to a verification threshold). When the PPE is missing or worn incorrectly, an alarm signal is generated to prevent the target user from entering. When the PPE is present and worn correctly, an alarm signal is generated to allow the target user to enter.

[0058] As shown in operation 305, an apparatus (e.g., computing device 200) includes apparatus such as processor 202, communication circuitry 208, image processing circuitry 210, etc., for receiving first image data of FOV 103 of first imaging device 102, the first image data including entry location 107. As described above, first imaging device 102 may be configured to capture FOV 103 of first imaging device 102 as first image data, which is a 2D or 3D representation of FOV 103. By way of example, first imaging device 102 may be positioned and / or oriented proximate entry location 107 such that entry location 107 is within FOV 103.

[0059] The first image data generated by the first imaging device 102 may include numerical values ​​representing coordinates of vertices forming a polygon within the field of view of the first imaging device 102. For example, in some embodiments, the first image data may include 3D image data associated with the relative position of a particular vertex (e.g., x-coordinate and y-coordinate) within the FOV 103, and may also include numerical values ​​of coordinates associated with the relative distance (e.g., depth or z-coordinate) between the first imaging device 102 and a target (e.g., an object within the FOV 103). As a specific example, the first image data may be stored in a polygon file format (PLY), which describes an object as a collection of vertices, faces, etc. and various attributes attached to these elements (e.g., color, normal direction, etc.). The 3D image data stored as a PLY file or otherwise stored may include descriptions of manually digitized objects, polygonal objects from a modeling program, range data, triangles from marching cubes (e.g., isosurfaces from volume data), terrain data, radiosity models, etc. Additionally, exemplary attributes that may be generated as 3D image data and stored with exemplary objects as PLY files may include color, surface normals, texture coordinates, transparency, range data confidence, and / or other attributes of polygon front and / or back faces.

[0060] Although described with reference to 3D image data, the present disclosure also contemplates that the first image data generated by the first imaging device 102 may include any attributes or parameters based on the properties of the first imaging device 102. By way of example, in some embodiments, the first imaging device 102 may include a barcode scanner, a camera configured to scan barcodes (e.g., a camera of a mobile device or other device), or any other device capable of being used with the scannable indicia 109. As described below with reference to Figure 4 As described above, in some embodiments, the first imaging device 102 may be configured to generate first image data that captures a scannable marker 109 located within the FOV 103 of the first imaging device 102. In such embodiments, the first image data generated by the first imaging device 102 may identify the presence or absence of such a scannable marker 109, and in conjunction with the computing device 200 determine an entry condition based on the presence or absence of the scannable marker 109. In some embodiments, the scannable marker 109 may be presented via, for example, a display device (e.g., an LCD, a display of a mobile device, etc.).

[0061] In embodiments where the computing device 200 and the first imaging device 102 are housed with a common device or integrated device (e.g., the computing device 200 includes the first imaging device 102), the first image data may be received by the computing device 200 (e.g., internally transmitted, if any) as part of normal operation of the first imaging device 102. In other embodiments where the computing device 200 is located separately from the first imaging device 102, such as connected via a network 104, the computing device 200 may be configured to receive the first image data from the first imaging device 102 in response to the generation of the first image data. In other words, each instance of the generation of the first image data may be transmitted to the computing device 200 as it is generated. In other embodiments, the computing device 200 may request the first image data from the first imaging device 102 periodically (e.g., according to a defined rate).

[0062] In some embodiments, the first image data may be generated by the first imaging device 102 and / or transmitted to the computing device 200 in response to an interaction with an entry location 107 within the FOV 103 of the first imaging device. By way of example, the first user 101 may attempt to enter a healthcare facility that employs one or more features of the security system 100. Attempting to enter such a facility (e.g., scanning an identification badge, attempting to open a door, attempting to pass through an entry point, etc.) may cause the first imaging device 102 to capture one or more images (e.g., generate first image data) including the entry location 107. As described below, the generation of the first image data may be responsive to a change in the entry conditions of the entry location 107, such as a situation in which a scannable marker 109 within the FOV 103 of the first imaging device 102 becomes present or becomes absent. In other words, the first imaging device 102 may be configured to generate the first image data in a situation in which a scannable marker 109 is present within the FOV 103. Furthermore, in some embodiments, the first imaging device may continuously generate first image data, and in response to an entry attempt by the first user 101 or others, the computing device 200 may send a request to the first imaging device 102 for the first image data including the entry location 107 .

[0063] As shown in operation 310, an apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, machine learning circuit 212, scanning circuit 214, etc., for determining an entry condition for entry location 107 based on first image data. As defined above, entry location 107 may be associated with an entry condition indicating a state of entry location 107 and / or indicating a user's interaction with entry location 107. By way of example, an entry condition for an exemplary door (e.g., entry location 107) may indicate that the door is physically open, physically closed, partially open, open for use by some users, closed for use by some users, etc. In addition, the entry condition may indicate that a user is currently entering entry location 107, has previously entered entry location 107, and / or is requesting entry to entry location 107.

[0064] As described with reference to operation 305, the first image data may include 2D or 3D image data including a plurality of pixels, coordinates of vertices, faces, etc., and various attributes attached to these elements (e.g., color, normal direction, etc.). In such embodiments, the computing device 200 may perform image processing on the first image data via the image processing circuit 210 and / or the machine learning circuit 212 to determine the physical position or orientation of the entry location 107. As a continuing example, in some embodiments, the entry location 107 may refer to an entry door through which a user may enter and / or exit a healthcare facility. Thus, the entry condition of such an entry location 107 (e.g., an entry door) may refer to determining whether the entry door is open at operation 310. In such embodiments, the computing device 200 may analyze a plurality of first image data entries and, for example, compare the first image data to image data including the entry location 107 in an open position (e.g., generated as part of an initial setup, training procedure, or otherwise).

[0065] In some embodiments, the entry condition may be determined based on the user's interaction with the entry location 107. By way of example, the first image data generated by the first imaging device 102 (or the second imaging device 105 described below) may also capture the first user 101 near the entry location 107. As a specific example, the first imaging device 102 may capture an image (e.g., generate the first image data) that includes a plurality of users queuing to enter the healthcare facility via an entry door (e.g., the entry location 107) of the healthcare facility. Similar to the image processing techniques described above with reference to the comparison between the image data, the computing device 200 may determine the entry condition of the entry location 107 based on one or more users attempting to enter the entry location 107 via the image processing circuitry 210 and / or the machine learning circuitry 212.

[0066] As described above, in some embodiments, the first imaging device 102 may include a barcode scanner, a camera configured to scan barcodes, or any other device capable of being used with the scannable indicia 109. Figure 4As described in the operation of the first imaging device 102, the first imaging device 102 may be configured to generate first image data that captures a scannable marker 109 located within the FOV 103 of the first imaging device 102. In such embodiments, the first image data generated by the first imaging device 102 may identify the presence or absence of such a scannable marker 109 and, in conjunction with the computing device 200, determine an entry condition based on the presence or absence of the scannable marker 109. For example, the first imaging device 102 may include a barcode scanner or camera configured to continuously generate first image data that includes a scannable marker 109 associated with an entry door (e.g., entry location 107). In some embodiments, the scannable marker 109 is positioned such that the presence of the scannable marker 109 indicates that the entry door is open (e.g., a barcode that is visible only in a case where the entry door is open). In other embodiments, the scannable marker 109 may be positioned such that the absence of the scannable marker 109 indicates that the entry door is open (e.g., a barcode that is intact or visible only in a case where the entry door is closed). In such embodiments, the determination of the entry condition at operation 310 may be a tractable determination, wherein the presence or absence of the scannable marker 109 may be determined by the computing device 200 without requiring a significant amount of processing resources. As described above, while described herein with reference to the scannable marker 109 being physically associated with the entry location 107, the present disclosure also contemplates that the scannable marker 109 may additionally or alternatively refer to a digital representation of the scannable marker 109. In other words, the execution of operations 305 and 310 may not burden the computing resources of the computing device 200, and thus may be used to gate or buffer the execution of the operations described below.

[0067] In the case where the entry condition indicates that the first user 101 is attempting to enter the entry location 107, as shown in operation 315, the device (e.g., computing device 200) includes means such as processor 202, communication circuit 208, image processing circuit 210, etc., for receiving second image data of the field of view 106 of the second imaging device 105, the second image data including the first user 101 for which a safety determination is to be performed. As described above, the second imaging device 105 can be configured to capture the FOV 106 of the second imaging device 105 as the second image data, which is a 2D or 3D representation of the FOV 106. By way of example, the second imaging device 105 can be positioned and / or oriented to capture a user attempting to enter the entry location 107.

[0068] Similar to the first image data, the second image data generated by the second imaging device 105 may include numerical values ​​representing coordinates of vertices forming a polygon within the field of view of the second imaging device 105. For example, in some embodiments, the second image data may include 3D image data associated with the relative position of a particular vertex (e.g., x-coordinate and y-coordinate) within the FOV 106, and may also include numerical values ​​of coordinates associated with the relative distance (e.g., depth or z-coordinate) between the second imaging device 105 and a target (e.g., an object within the FOV 106). As a specific example, the second image data may also be stored in a polygon file format (PLY), which describes an object as a collection of vertices, faces, etc. and various attributes attached to these elements (e.g., color, normal direction, etc.). The 3D image data stored as a PLY file or otherwise stored may include descriptions of manually digitized objects, polygonal objects from a modeling program, range data, triangles from marching cubes (e.g., isosurfaces from volume data), terrain data, radiosity models, etc. Additionally, exemplary attributes that may be generated as 3D image data and stored as a PLY file with the exemplary object may include color, surface normal, texture coordinates, transparency, range data confidence, and / or other attributes of polygon front and / or back faces. Although described with reference to 3D image data, the present disclosure also contemplates that the second image data generated by the second imaging device may also include any attributes or parameters based on the properties of the second imaging device 105.

[0069] In embodiments where the computing device 200 and the second imaging device 105 are housed with a common device or integrated device (e.g., the computing device 200 includes the second imaging device 105), the second image data may be received by the computing device 200 (e.g., internally transmitted, if any) as part of normal operation of the second imaging device 105. In other embodiments where the computing device 200 is located separately from the second imaging device 105, such as connected via the network 104, the computing device 200 may be configured to receive the second image data from the second imaging device 105 in response to the generation of the second image data. In other words, each instance of the second image data generation may be transmitted to the computing device 200 as it is generated. In other embodiments, the computing device 200 may periodically (e.g., according to a defined rate) request the second image data from the second imaging device 105. In some embodiments, the second image data received at operation 315 may include image data entries associated with the device of the first user 101. In other words, the first user 101 may be associated with a display device, mobile device, etc. that is present within the FOV 106 of the second imaging device 105 (e.g., visible to the second imaging device 105). In some embodiments, such a display device may present information identifying the first user 101 when captured by the second imaging device 105. In other words, the present disclosure contemplates that a digital representation of the user may be provided to the second imaging device 105 at operation 315.

[0070] As described above, the determination of the entry condition at operation 310 may be used to gate or otherwise buffer utilization of the second imaging device 105 in order to reduce the computational burden on the computing device 200. Thus, in some embodiments, the second image data of operation 415 may be generated by the second imaging device 105 and / or transmitted to the computing device 200 only in response to a determination that the first user 101 is attempting to enter the location. By way of example, the first user 101 may attempt to enter a healthcare facility that employs one or more features of the security system 100. Attempting to enter such a facility (e.g., scanning an identification badge, attempting to open a door, attempting to pass through an entry point, etc.) may cause the second imaging device 105 to capture one or more images (e.g., generate second image data) that include the first user 101. Although described herein with reference to second image data that includes the first user 101 in the FOV 106, the present disclosure also contemplates that the second image data may include multiple users, one or more of which may attempt to enter the entry location 107.

[0071] As shown in operation 320, an apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for generating a safety parameter associated with the first user 101 within the field of view 106 of the second imaging device 105, the safety parameter indicating the presence and location of the safety equipment of the first user 101. As described above, in some embodiments, the user may be required to wear safety equipment or PPE, which is designed to protect the user from various hazards associated with a particular environment. For example, the user may be required to wear a mask in order to enter a medical or healthcare facility, thereby reducing or preventing the spread of infectious diseases, bacteria, viruses, etc. Therefore, the second image data generated at operation 315 including the first user 101 may also include second image data (e.g., captured images) of the safety equipment (e.g., mask) positioned relative to the first user 101. In other words, the second image data may include numerical values ​​associated with the coordinates of the vertices of the polygon associated with the mask (e.g., PPE). Although described below with reference to an exemplary mask detection implementation, the present disclosure also contemplates that the safety parameters generated at operation 320 may take into account any safety equipment or PPE based on the intended application of system 100 .

[0072] The safety parameters may be generated based on the second image data, and may be based on the presence and relative positioning of the safety device relative to the first user 101. As a continuing example, the safety parameters may indicate or be based on the relative positioning of the mask relative to the user's mouth and nose. In other words, a properly worn or positioned mask may adequately cover (e.g., compared to relevant thresholds described below) the user's mouth to reduce or prevent matter (e.g., bacteria, viruses, particles, etc.) from entering the first user's mouth. A missing or improperly worn mask may not adequately cover (e.g., compared to relevant thresholds described below) the user's mouth. As described below with reference to Figure 5 As described, determination of the safety parameter may include comparing the second image data with calibrated image data associated with the first user 101, and / or providing the second image data to a machine learning model (e.g., an artificial neural network, a convolutional neural network, or other machine learning system) to analyze the second image data to identify the presence and relative positioning of a safety device (e.g., a mask) and output an associated confidence value (e.g., a safety parameter).

[0073] As referenced below Figure 5As described, in some embodiments, the second image data may be compared to calibrated image data associated with the first user 101. In such embodiments, as part of an initial registration or training procedure, the second imaging device 105 may capture an image of the first user 101 in which the first user 101 is properly wearing the associated safety equipment or PPE (e.g., generating the second image data). The collection of captured images may be analyzed by the computing device 200 via the image processing circuit 210 and / or the machine learning circuit 212 to generate calibrated image data of the first user 101. The calibrated image data of the first user 101 may be used as a benchmark, and the second image data generated by the second imaging device 105 including the first user 101 may be compared to the benchmark. In such embodiments, the safety parameter generated at operation 320 may refer to a score or digital representation of the similarity between the second image data and the calibrated image data of the first user 101. In other words, the computing device 200 may compare the numerical values ​​contained in the second image data indicating the coordinates and attributes of the vertices forming a polygon within the FOV 106 of the second imaging device 105 including the first user 101 with the numerical values ​​contained in the calibrated image data of the first user 101.

[0074] As described above, in some embodiments, the second image data may be provided to a machine learning model, which may be trained, for example, based on image data generated by a second imaging device for multiple users. For example, an artificial neural network or convolutional neural network used by the security system 100 may be iteratively trained based on the second image data, which includes multiple users and associated safety devices at different positions relative to the respective users. In other words, such an artificial neural network or convolutional neural network may be trained based on sufficient second image data to determine the position of the first user's mouth and the presence and position of the safety device or PPE (e.g., mask) relative to the first user's mouth. As a specific example, in some embodiments, the safety parameter may refer to the confidence of the computing device 200 that the safety device or PPE is correctly positioned (e.g., the confidence of the artificial neural network or convolutional neural network). For example, the system may be 60% confident that the first user's mask is correctly positioned relative to the user's mouth, resulting in a safety parameter of 0.6 or 60%.

[0075] Thereafter, as shown in operation 325, the device (e.g., computing device 200) includes devices such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for comparing the security parameters with the verification thresholds associated with the security device. In order to define the presence and proper positioning of the security device relative to the first user 101, the computing device 200 may employ various verification thresholds associated with the corresponding security device. By way of example, a mask-related device may be based on or otherwise indicate an associated verification threshold related to the positioning of the security device relative to the user's mouth (e.g., a position that adequately covers the user's mouth). In some embodiments, each safety device may also include devices of different sizes, shapes, types, etc. For example, the mask may vary in length, shape, cross-sectional area, material, etc. Therefore, the present disclosure contemplates that the verification thresholds and security parameters described herein may be further configured for security devices of specific sizes, shapes, types, etc. In some embodiments, the verification thresholds described herein may be set in accordance with applicable industry standards or regulations, set by a system administrator or setup program, or determined based on iterative analysis of the second image data by a machine learning model.

[0076] Continuing with reference to operation 325, the verification threshold associated with the mask-related safety equipment or PPE may, for example, define a minimum confidence value of 0.85 or 85%. In such examples, the safety parameters generated at operation 320 may be compared to the verification threshold to determine whether the safety parameters meet the verification threshold. For example, if the safety parameters generated at operation 320 exceed 90%, the safety parameters meet the verification threshold, which indicates the system's confidence that the mask of the first user exists and correctly covers the mouth of the first user. In such embodiments, the safety system 100 may determine that the positioning of the safety equipment of the first user 101 is sufficient to reduce or prevent the spread of infectious diseases, and in some embodiments as described herein, the first user 101 may be allowed to enter the entry location 107 (e.g., to enter a healthcare facility, etc.). Although described herein with reference to a verification threshold of 0.85 or 85%, the present disclosure also contemplates that the verification threshold may define any associated confidence value or parameter based on the intended application of the safety system 100.

[0077] In some embodiments, the comparison of the security parameter to the verification threshold may rely on or otherwise utilize various ratios between, for example, parameters associated with the first user 101 (e.g., the dimensions of the first user (height, width, length, etc.)). By way of example, the image processing circuit 210 may be configured to analyze the second image data and determine the height associated with the first user 101. Additionally or alternatively, the communication circuit 208 may be configured to query a calibration image database 110 that includes, for example, data indicating the height of the first user 101. In such exemplary embodiments, the security parameter generated at operation 320 may refer to the position of the PPE item related to the height associated with the first user 101. In other words, the security parameter may, for example, indicate that the center of the PPE item is located at approximately 90% of the top of the height toward the first user 101.

[0078] Similarly, the verification threshold may refer to a percentage range (e.g., between 85% and 95% of the total height of the first user 101) such that the safety parameters falling within the range meet the verification threshold. As a specific example, the verification threshold may be defined based on, for example, the average position of the user's eyes relative to the user's total height, such that the PPP (e.g., goggles or other glasses) determined to be positioned at such an average position of the user's eyes meets the verification threshold. In some embodiments, the PPE item may include various markings (e.g., grids, axes, etc.) that may be captured by the second imaging device 105 and used to facilitate the determination of the safety parameters described herein. For example, the first user 101 may wear a mask including a grid pattern (e.g., visible to the user, in invisible ink or otherwise), which may be used to determine the presence of the mask and the relative positioning of the mask relative to the face of the first user (e.g., determining whether the grid is aligned with the centerline of the user). Although described herein with reference to a comparison between the height of the first user 101 and the positioning of the PPE item relative to that height, the present disclosure also contemplates that any parameters, dimensions, measurements, ratios, etc. associated with the first user 101 may be used based on the intended application of the system 100.

[0079] In the case where the safety parameters fail to meet the verification threshold, as shown in operation 330, the device (e.g., computing device 200) includes means such as processor 202, communication circuit 208, input / output circuit 206, etc., for generating an alarm signal. The alarm signal may indicate non-compliance of the first user 101 in terms of the presence and positioning of the safety equipment. In some embodiments, generating the alarm signal may include generating a display including the safety parameters for display, such as through input / output circuit 206, for viewing by an operator, administrator, or other user of the safety system 100. In some embodiments, the alarm signal may be transmitted, such as by communication circuit 208, to a user device associated with the first user 101. In such embodiments, the alarm signal may be used to notify the user of a potential safety issue associated with the absence or positioning of the safety equipment or PPE of the first user.

[0080] In some embodiments, the alarm signal generated at operation 330 may also include instructions for preventing the first user 101 from entering the entry location 107. As described above, the computing device 200, the first imaging device 102, and / or the second imaging device 105 may be located near the entry location 107 to ensure security verification before allowing a particular user to enter. As a continuing example, one or more devices of the security system 100 may be positioned at an entry door of a healthcare facility to confirm the proper wearing of the security device before allowing entry to such a facility. Therefore, in the event that the security parameters fail to meet the verification threshold, the alarm signal generated at operation 330 may also include instructions to one or more systems of the entry location 107 (e.g., an entry gate, door, turnstile, etc.) that prevent the first user 101 from entering (e.g., physically entering, electronically entering, etc.) the entry location 107. In other words, as described above, the computing device 200 may be configured to determine that incorrect or improper wearing of the safety device (e.g., incorrect positioning of the safety device relative to the first user 101) causes the safety device to be unable to adequately protect the first user 101, and may prevent the first user 101 from entering a location, system, etc. that may be harmful to the first user 101 or that otherwise requires correct safety device positioning.

[0081] In some embodiments, generating the alarm signal at operation 330 may also include changing an entry condition of the entry location 107. By way of example, the entry condition determined at operation 310 may indicate that an entry door (e.g., entry location 107) is open (e.g., an open state condition). In the event that the security parameter fails to meet the verification threshold, the alarm signal may be used to change the entry condition by causing the entry door (e.g., entry location 107) to close (e.g., a closed state condition). By way of example, the computing device 200 may send instructions to one or more systems of the entry location 107 (e.g., an entry gate, door, turnstile, etc.), which causes the entry location 107 to prevent the first user 101 from entering (e.g., physically entering, electronically entering, etc.) the entry location 107 when the entry location 107 may have previously allowed such entry.

[0082] Figure 4 A flow chart including a series of operations for determining entry conditions is shown. As described above, Figure 4 The operations shown may be performed, for example, by, with the assistance of, and / or under the control of a device (e.g., computing device 200). In this regard, the execution of these operations may invoke one or more of processor 202, memory 204, input / output circuitry 206, communication circuitry 208, image processing circuitry 210, machine learning circuitry 212, and / or scanning circuitry 214. As described below, an image captured from first imaging device 102 (e.g., first image data) may be analyzed to determine the absence or presence of a scannable indicia 109 (e.g., a barcode or other indicia). In some embodiments, the presence of such a barcode (e.g., a scannable indicia 109) may indicate that entry location 107 is in an open state (e.g., an exemplary entry door is open). In other embodiments, the absence of such a barcode (e.g., a scannable indicia 109) may indicate that entry location 107 is in an open state (e.g., an exemplary entry door is open). The present disclosure contemplates that the FOV 103 of the first imaging device 102 and / or the FOV 106 of the second imaging device 105 may be blocked by an item, one or more users, etc., near the entry location 107. Accordingly, the system may be configured to generate an alarm signal in situations where at least a portion of the FOV 103, 105 is blocked, obstructed, etc. In some embodiments, such an alarm signal may request that additional users be removed from the FOV 105.

[0083] As shown in operation 405, an apparatus (e.g., computing device 200) includes apparatus such as processor 202, communication circuitry 208, image processing circuitry 210, etc., for receiving first image data of FOV 103 of first imaging device 102, the first image data including entry location 107. As described above with reference to operation 305, first imaging device 102 may be configured to capture FOV 103 of first imaging device 102 as first image data, the first image data being a 2D or 3D representation of FOV 103. By way of example, first imaging device 102 may be positioned and / or oriented proximate entry location 107 such that entry location 107 is within FOV 103. In the example of FIG. Figure 4 In some embodiments shown, the first imaging device 102 may include a barcode scanner, a camera configured to scan barcodes, or any other device capable of being used with the scannable indicia 109. Thus, the first imaging device 102 may be configured to generate first image data that captures the scannable indicia 109 within the FOV 103 of the first imaging device 102. As described below with reference to operations 410 and 415, in some cases, the presence of the scannable indicia 109 may indicate an open state entry condition into the location 107. As described below with reference to operations 420 and 425, in some cases, the absence of the scannable indicia 109 may indicate an open state entry condition into the location 107.

[0084] In the case where the presence of the scannable indicia 109 may indicate an open state entry condition, the device (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, scanning circuit 214, etc., for identifying an instance of the scannable indicia 109 in the first image data, as shown in operation 410. As described above, the first imaging device 102 may be configured to capture an image (e.g., generate first image data) that includes a scannable indicia 109 such as a barcode. Therefore, the first imaging device 102 and / or computing device 200 may be configured to identify an instance of the scannable indicia 109 in the first image data. As a continuing example, an entry door (e.g., entry location 107) may include a scannable indicia 109 (e.g., a barcode) that is only visible in a condition where the entry door is open. As a specific example, the scannable indicia 109 may be positioned on a surface such that when the entry door is closed, the entry door blocks or otherwise obstructs the line of sight between the first imaging device 102 and the scannable indicia 109. In some embodiments, the line of sight between the first imaging device 102 and the scannable indicia 109 may additionally or alternatively be blocked by another user (not shown) located near the entry location 107 or elsewhere.

[0085] In such embodiments, as shown at operation 415, an apparatus (e.g., computing device 200) includes means such as processor 202, communication circuit 208, image processing circuit 210, scanning circuit 214, etc., for determining an open state entry condition of entry location 107 based on scannable indicia 109. As a continuing example, first imaging device 102 may generate first image data including scannable indicia 109 at operation 405. Computing device 200 may identify this instance of scannable indicia 109 via scanning circuit 214, and at operation 415, determine that the presence of such scannable indicia 109 indicates an open state entry condition of entry location 107. Although described herein with reference to a barcode that may be blocked by an entry door, the present disclosure also contemplates that any type of scannable indicia 109 may be positioned at any position relative to entry location 107 based on the intended application of system 100.

[0086] In the case where the absence of the scannable indicia 109 may indicate an open state entry condition, the apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, scanning circuit 214, etc., for identifying the absence of the scannable indicia 109 in the first image data, as shown in operation 420. As described above, the first imaging device 102 may be configured to capture an image (e.g., generate first image data) that includes a scannable indicia 109 such as a barcode. Thus, the first imaging device 102 and / or computing device 200 may be configured to identify the absence of the scannable indicia 109 in the first image data. As a continuing example, an entry door (e.g., entry location 107) may include a scannable indicia 109 (e.g., a barcode) that is only visible in a condition where the entry door is closed. As a specific example, the scannable indicia 109 may be partially positioned on a portion of the entry door and partially positioned on a surface adjacent to the entry door, such that the scannable indicia 109 can be read by the first imaging device 102 when the entry door is closed.

[0087] In such embodiments, as shown at operation 425, an apparatus (e.g., computing device 200) includes means such as processor 202, communication circuit 208, image processing circuit 210, scanning circuit 214, etc., for determining an open state entry condition into the entry location 107 based on the absence of a scannable marker 109. As a continuing example, the first imaging device 102 may continuously generate first image data of the entry location 107 at operation 405. The computing device 200 may identify such absence of a scannable marker 109 in the first image data via scanning circuit 214, and at operation 415, determine that the absence of such a scannable marker 109 indicates an open state entry condition into the entry location 107. As described above, the present disclosure contemplates that any type of scannable marker 109 may be positioned at any position relative to the entry location 107 based on the intended application of the system 100.

[0088] In any embodiment, the scannable indicia 109 (e.g., a barcode, etc.) may be received by the computing device 200 and analyzed to determine the PPE associated with the particular entry location 107. In other words, the scannable indicia 109 captured by the first imaging device 102 may be decoded to determine one or more PPE items required for a user to enter the particular entry location 107. By way of example, the entry location 107 may be associated with a healthcare facility, such that the first image data including the scannable indicia 109, when analyzed by the scanning circuit 214 of the computing device 200, may indicate that a user attempting to enter the healthcare facility must properly wear a mask or other facial covering. In other embodiments, the scannable indicia 109, when analyzed by the scanning circuit 214 of the computing device, may list an identifier (e.g., a PPE code, etc.). In such embodiments, the computing device may compare such identifiers with one or more repositories or databases that contain PPE requirements associated with the corresponding identifiers. In this way, the scannable indicia 109 associated with a particular entry point may remain unchanged, but the PPE requirements associated with the entry point may be dynamically updated.

[0089] Figure 5 A flow chart including a series of operations for generating security parameters is shown. As described above, Figure 5 The operations illustrated may be performed, for example, by, with the assistance of, and / or under the control of an apparatus (e.g., computing device 200). In this regard, performance of the operations may invoke one or more of processor 202, memory 204, input / output circuitry 206, communication circuitry 208, image processing circuitry 210, machine learning circuitry 212, and / or scanning circuitry 214.

[0090] As shown in operation 505, an apparatus (e.g., computing device 200) includes apparatus such as processor 202, communication circuitry 208, image processing circuitry 210, etc., for receiving second image data of a field of view of a second imaging device 105, the second image data including the first user 101. As described above with reference to operation 315, the second imaging device 105 may be configured to capture the FOV 106 of the second imaging device 105 as second image data, the second image data being a 2D or 3D representation of the FOV 106. By way of example, the second imaging device 105 may be positioned and / or oriented to capture a user attempting to enter the entry location 107. The generated second image data may also include image data (e.g., the captured image) of a safety device (e.g., a mask) positioned relative to the first user 101. In other words, the second image data may include numerical values ​​associated with coordinates of vertices of a polygon associated with the mask (e.g., PPE). Although described below with reference to an exemplary mask detection implementation, the present disclosure also contemplates the use of a second image data device 105 for detecting a user attempting to enter the entry location 107. Figure 5 The generated safety parameters may take into account any safety equipment or PPE based on the intended application of the system 100 .

[0091] In some embodiments, as shown in operation 510, an apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for querying a calibration image database to retrieve calibrated image data of the first user 101. As defined above, calibration image database 110 may refer to a storage space or repository of image data generated by one or more of the first imaging device 102 or the second imaging device 105. By way of example, as part of an initial calibration or training procedure, calibration image database 110 may be configured to receive and store image data associated with a plurality of users. In some embodiments, calibration image database 110 may be stored locally (e.g., by memory 204 of computing device 200), such that the query at operation 410 refers to an internal communication. In other embodiments, calibration image database 110 may be stored remotely from computing device 200, such that the query at operation 510 refers to an external communication via network 104 or otherwise.

[0092] Thereafter, as shown in operation 515, an apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for comparing second image data including first user 101 with calibrated image data associated with first user 101. In such embodiments, as part of an initial registration procedure or other procedure, second imaging device 105 may capture an image of first user 101 in which first user 101 is properly wearing associated safety equipment or PPE (e.g., generating second image data). This collection of captured images may be collectively analyzed by computing device 200 via image processing circuit 210 and / or machine learning circuit 212 to generate calibrated image data of first user 101 for a particular safety equipment or PPE. The calibrated image data of first user 101 may be used as a benchmark, and second image data generated by second imaging device 105 including first user 101 may be compared with the benchmark.

[0093] In such embodiments, as shown in operation 520, an apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for generating a security parameter based on a comparison between the second image data and the calibrated image data. By way of example, computing device 200 may compare numerical values ​​contained within the second image data indicating coordinates and attributes of vertices forming a polygon within FOV 106 of second imaging device 105 including first user 101 with numerical values ​​contained within calibrated image data of first user 101. Such a comparison may generate a security parameter that is a score or numerical representation of the similarity between the second image data and the calibrated image data of first user 101. As a specific example, computing device 200 may determine that the second image data is 60% similar to the calibrated imaging data (e.g., 60% of the pixels, coordinates, etc. are substantially the same as the calibrated image data), such that the security parameter is 0.6 or 60%.

[0094] In other embodiments, as shown in operation 525, the device (e.g., computing device 200) includes a device such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for providing the second image data to a machine learning model (e.g., an artificial neural network, a convolutional neural network, or other machine learning system) to analyze the second image data to identify the presence and relative positioning of the safety device (e.g., a mask) and output an associated confidence value (e.g., a safety parameter). As described above, in some embodiments, the second image data can be provided to a machine learning model, which can be trained, for example, based on image data generated by the second imaging device 105 for multiple users. For example, an artificial neural network used by the security system 100 can be iteratively trained based on the second image data, which includes multiple users and associated safety devices at different positions relative to the respective users. In other words, such an artificial neural network or convolutional neural network can be trained based on sufficient second image data to determine the position of the first user's mouth and the presence and position of the safety device or PPE (e.g., a mask) relative to the first user's mouth.

[0095] During the training of such exemplary machine learning models (e.g., artificial / convolutional neural networks, etc.), a plurality of second image data may be iteratively provided to the model so that the model may be configured to determine patterns contained in a plurality of numerical values ​​within the coordinates defined by the second image data over time. In other words, the machine learning model may be configured to determine a correlation or pattern associated with a numerical value at a specific position within the second image data in order to determine the relative position of the user captured by the second image data. For example, image data associated with a plurality of users with or without PPE may be provided to the machine learning model, and image data associated with a plurality of users each having various forms of PPE may be further provided to it. In some cases, the training may be supervised, wherein the machine learning model determines the specific type and wearing condition of the PPE, and confirmation is requested by the user (e.g., a system administrator, etc.). Alternatively or in addition thereto, the machine learning model may be trained based on a data corpus including users with and without PPE. In other embodiments, the machine learning model may be subject to unsupervised training, reinforcement learning, semi-supervised training, etc., in order to analyze the second image data.

[0096] In such embodiments, as shown in operation 530, the apparatus (e.g., computing device 200) includes means such as processor 202, image processing circuit 210, machine learning circuit 212, etc., for generating a safety parameter based on the output of the machine learning model. As a specific example, in some embodiments, the safety parameter may refer to the confidence of computing device 200 that the safety device or PPE is correctly positioned (e.g., the confidence of an artificial neural network or convolutional neural network). For example, the system may be 60% confident that the mask of the first user is correctly positioned relative to the user's mouth, resulting in a safety parameter of 0.6 or 60%.

[0097] In some embodiments, the training of the machine learning model may enable the model to not only determine the wearing condition of the specified PPE (e.g., determine whether the mask is worn correctly), but also be trained to analyze the second image data and determine the type of PPE worn by the first user 101, if any. In this way, the computing device 200 may compare the determined type of PPE with one or more repositories or databases storing PPE requirements with corresponding entry locations, as described above. In some embodiments, since the machine learning model is provided with image data indicating multiple users and multiple PPE types, the machine learning model may be further configured to receive the second image data including multiple users, and determine the type of PPE associated with the specific entry location 107 based on the determined PPE worn by one or more users. By way of example, the computing device 200 may be configured to receive the second image data including multiple users, and based on the training of one or more machine learning models utilized by the computing device 200, the computing device 200 may determine that one or more users in the second image data are wearing a mask. In response, the computing device 200 may determine that the entry location 107 near the second imaging device 105 may require the user to wear a mask before entering. Such determination may be further confirmed by a system administrator, or compared to a repository or database containing PPE requirements as described above.

[0098] therefore, Figures 3 to 5A flow chart describing the operation of the device, method and computer program product according to the exemplary embodiments contemplated herein is shown. It should be understood that each flowchart frame and the combination of flowchart frames can be implemented by various devices (such as hardware, firmware, processor, circuit and / or other devices associated with the execution of software including one or more computer program instructions). For example, one or more operations in the above-mentioned operations can be implemented by a device that executes computer program instructions. In this regard, the computer program instructions can be stored by the memory 204 of the computing device 200 and executed by the processor 202 of the computing device 200. It should be understood that any such computer program instructions can be loaded onto a computer or other programmable device (e.g., hardware) to produce a machine so that the resulting computer or other programmable device realizes the function specified in the flowchart frame. These computer program instructions can also be stored in a computer-readable memory, which can instruct a computer or other programmable device to work in a particular manner so that the instructions stored in the computer-readable memory produce a product, and the execution of these instructions realizes the function specified in the flowchart frame. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus, thereby producing a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide operations for implementing the functions specified in the flowchart blocks.

[0099] The flowchart blocks support the combination of devices for performing the specified functions and the combination of operations for performing the specified functions. It should be understood that one or more blocks of the flowchart and the combination of blocks in the flowchart can be implemented by a hardware-based special-purpose computer system or a combination of special-purpose hardware that performs the specified functions through computer instructions.

Claims

1. A method for entry-related security determination, the method comprising: receiving first image data of a field of view of a first imaging device, the first image data including an entry location; determining an entry condition of the entry location by analyzing the first image data, wherein the entry condition indicates an interaction of the first user with the entry location or a state of the entry location, wherein determining the entry condition of the entry location further comprises identifying whether a scannable mark exists in the first image data, and determining an open state entry condition of the entry location based on the scannable mark; when the determined entry condition indicates at least one of the following: the first user attempting to enter the entry location and the entry location being in an open state entry condition, causing a second imaging device to capture second image data of a field of view of the second imaging device, wherein the second image data includes the first user for whom a security determination is to be performed; generating a security parameter associated with the first user within a field of view of the second imaging device, wherein the security parameter indicates the presence and location of a security device of the first user; comparing the security parameter to a verification threshold; generating an alarm signal in the event that the security parameter fails to satisfy the validation threshold; and In response to generating the alarm signal, the first user is prevented from entering the entry location. 2 . The method of claim 1 , further comprising generating a notification for display, the notification including the security parameter. The method of claim 1 , wherein generating the alarm signal further comprises changing an entry condition of the entry location. 4 . The method of claim 1 , wherein generating the security parameter further comprises comparing the second image data including the first user with calibrated image data associated with the first user.

5. The method of claim 1, wherein generating the security parameter further comprises providing the second image data to a machine learning model.

6. A system for entry-related security determinations, the system comprising: a first imaging device configured to generate first image data of a first imaging device field of view, the first image data including an entry location; a second imaging device configured to generate second image data of a second imaging device field of view, the second image data including the first user for which a security determination is to be performed; as well as a computing device operably connected to the first imaging device and the second imaging device, wherein the computing device is configured to: receiving the first image data; determining an entry condition of the entry location by analyzing the first image data, wherein the entry condition indicates an interaction of the first user with the entry location or a state of the entry location, wherein determining the entry condition of the entry location further comprises identifying whether a scannable mark exists in the first image data, and determining an open state entry condition of the entry location based on the scannable mark; When the determined entry condition indicates at least one of the following: the first user attempting to enter the entry location and the entry location being in an open state entry condition, causing the second imaging device to capture second image data of the second imaging device's field of view; generating a security parameter associated with the first user within a field of view of the second imaging device, wherein the security parameter indicates the presence and location of a security device of the first user; comparing the security parameter to a verification threshold; generating an alarm signal if the security parameter fails to meet the validation threshold, and In response to generating the alarm signal, the first user is prevented from entering the entry location. 7 . The system of claim 6 , wherein the computing device is further configured to, if the security parameter fails to satisfy the verification threshold, generate a notification for display, the notification including the security parameter.

8. The system of claim 6, wherein the computing device is further configured to prevent the first user from entering the entry location.

9. The system of claim 6, wherein the computing device is further configured to change the entry condition of the entry location.

10. The system of claim 6, wherein the computing device is configured to generate the security parameter by comparing the second image data including the first user with calibrated image data associated with the first user.

11. The system of claim 6, wherein the computing device is configured to generate the security parameter by providing the second image data to a machine learning model.

12. A non-transitory computer-readable storage medium for making an entry-related security determination using an apparatus, the non-transitory computer-readable storage medium storing instructions that, when executed, cause the apparatus to: receiving first image data of a field of view of a first imaging device, the first image data including an entry location; determining an entry condition of the entry location by analyzing the first image data, wherein the entry condition indicates an interaction of the first user with the entry location or a state of the entry location, wherein determining the entry condition of the entry location further comprises identifying whether a scannable mark exists in the first image data, and determining an open state entry condition of the entry location based on the scannable mark; when the determined entry condition indicates at least one of the following: the first user attempting to enter the entry location and the entry location being in an open state entry condition, causing a second imaging device to capture second image data of a field of view of a second imaging device, wherein the second image data includes the first user for whom a security determination is to be performed; generating a security parameter associated with the first user within a field of view of the second imaging device, wherein the security parameter indicates the presence and location of a security device of the first user; comparing the security parameter to a verification threshold; generating an alarm signal if the security parameter fails to meet the validation threshold, and In response to generating the alarm signal, the first user is prevented from entering the entry location.

13. The non-transitory computer-readable storage medium of claim 12, wherein the non-transitory computer-readable storage medium stores instructions that, when executed, cause the apparatus to: comparing the second image data including the first user with calibrated image data associated with the first user; or The second image data is provided to a machine learning model.

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