Method, device, equipment and medium for monitoring tailgating behavior in gate channel

Abstract

The present application discloses a method, device, equipment and medium for monitoring tailgating behavior in a gate channel, which relates to the field of image processing technology and is applied to a server equipped with a target tailgating behavior recognition model. The method comprises: establishing a target node corresponding to each pedestrian in a video image corresponding to the area to be recognized and a node identifier corresponding to the target node, tracking the pedestrian's trajectory to obtain a real-time trajectory, and saving each target node and node identifier to a preset relationship network; identifying the behavior state corresponding to each pedestrian, and determining whether the behavior state meets a preset condition. If so, the corresponding node identifier is eliminated in the preset relationship network to stop status recognition of the pedestrian corresponding to the target node; if not, determining that the current pedestrian has tailgating behavior, and transmitting the pedestrian's real-time trajectory and pedestrian image to a preset terminal device to initiate emergency response measures for the tailgating behavior. This can improve the accuracy of tailgating behavior recognition.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method, device, equipment and medium for monitoring tailgating behavior in a gate channel. Background Art

[0002] At present, there are four methods used to determine whether the behavior of pedestrians crossing the turnstile is legal, namely: infrared anti-tailing method, face recognition anti-tailing method, traditional physical three-roller turnstile anti-tailing method and AB door anti-tailing method. However, all of the above four methods have problems in certain aspects.

[0003] First, the infrared anti-tailing method is susceptible to environmental influences, is sensitive to obstacles, lacks an effective means of notification when tailing behavior is detected, and requires frequent maintenance and regular calibration. Second, the facial recognition anti-tailing method cannot accurately identify individuals in special circumstances, lacks an effective means of notification when tailing behavior is detected, has low pedestrian passage efficiency, and is integrated within the gate machine, making subsequent upgrades difficult, time-consuming, and costly. Furthermore, the traditional physical three-roller gate anti-tailing method has slow passage speeds, is inconvenient for pedestrians carrying luggage, cannot identify and track people, has high maintenance costs, and is not user-friendly for people with limited mobility. Finally, the AB gate anti-tailing method requires a lot of space and has low pedestrian passage efficiency. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for monitoring tailgating behavior in gate passages, which can improve the accuracy of tailgating behavior identification, track the trajectory of pedestrians with tailgating behavior, and improve the efficiency of controlling pedestrians with tailgating behavior. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a method for monitoring tailgating behavior in a gate channel, which is applied to a server configured with a target tailgating behavior recognition model, wherein the target tailgating behavior recognition model is a model trained based on an artificial intelligence model; wherein the method comprises:

[0006] Establishing target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, tracking the pedestrian's trajectory to obtain a real-time trajectory, and then saving each target node and corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm;

[0007] Identifying the behavior status corresponding to each of the pedestrians, and determining whether the obtained behavior status of the pedestrian meets a preset condition; if the behavior status meets the preset condition, eliminating the corresponding node identifier in the preset relationship network to stop status identification of the pedestrian corresponding to the corresponding target node;

[0008] If the behavior status does not meet the preset conditions, it is determined that the pedestrian is currently following the vehicle, and the real-time trajectory and pedestrian image corresponding to the pedestrian are transmitted to a preset terminal device to initiate emergency response measures for the following behavior.

[0009] Optionally, establishing target nodes corresponding to respective pedestrians in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and tracking the pedestrians to obtain real-time trajectories, includes:

[0010] Dividing the to-be-identified area in the real-time to-be-identified video using a preset area division rule to obtain an to-be-processed area, and processing the to-be-processed video image corresponding to the to-be-processed area using a preset resolution enhancement algorithm to obtain a target video image;

[0011] Target nodes corresponding to the pedestrians in the target video image and node identifiers corresponding to the target nodes are established by using several processes that can share information with each other, and the pedestrians are tracked to obtain real-time trajectories.

[0012] Optionally, the step of saving each target node and the corresponding node identifier to a preset relationship network includes:

[0013] Based on the information shared between the processes, the target nodes corresponding to each of the pedestrians and the node identifiers corresponding to the target nodes are aligned in real time, so that the target nodes and the node identifiers corresponding to the target nodes that have been aligned in real time are stored in the preset relationship network.

[0014] Optionally, the tracking the pedestrian to obtain a real-time trajectory includes:

[0015] Acquire a real-time video to be identified, and determine a pedestrian position corresponding to each pedestrian in the area to be identified in the real-time video to be identified;

[0016] A real-time trajectory corresponding to each pedestrian is determined based on the position of each pedestrian.

[0017] Optionally, the identifying of the behavior states corresponding to the pedestrians and determining whether the obtained behavior states of the pedestrians meet preset conditions, and if the behavior states meet the preset conditions, eliminating the corresponding node identifiers in the preset relationship network to stop status identification of the pedestrians corresponding to the corresponding target nodes, includes:

[0018] Identifying the behavior states corresponding to the pedestrians to obtain the behavior states corresponding to the pedestrians;

[0019] Determining whether the obtained behavior status of the pedestrians meets a preset condition, so as to obtain a judgment result corresponding to each of the pedestrians;

[0020] Sorting the plurality of judgment results in descending order of priority using a preset priority sorting algorithm to obtain a sorting result; wherein the priority corresponding to the judgment result indicating that the preset condition is satisfied is higher than the priority corresponding to the judgment result indicating that the preset condition is not satisfied;

[0021] Obtaining each of the judgment results from the sorting results in order from front to back;

[0022] If the judgment result obtained from the sorting result indicates that the preset condition is satisfied, the corresponding node identifier is eliminated from the preset relationship network to stop the state recognition operation for the pedestrian corresponding to the corresponding target node;

[0023] Correspondingly, if the behavior state does not meet the preset conditions, it is determined that the current pedestrian is in a tailgating behavior, including:

[0024] If the judgment result obtained from the sorting result indicates that the preset condition is not satisfied, it is determined that the pedestrian is currently following.

[0025] Optionally, identifying the behavior status corresponding to each of the pedestrians and determining whether the obtained behavior status of the pedestrians meets a preset condition includes:

[0026] The behavior status corresponding to each of the pedestrians is identified, and based on the behavior status, it is determined whether the behavior of the pedestrian crossing the gate is a tailgating behavior.

[0027] Optionally, the identifying the behavior status corresponding to each of the pedestrians and determining whether the pedestrian's behavior of crossing the gate is a tailgating behavior based on the behavior status includes:

[0028] Identifying the behavior states corresponding to the pedestrians, and determining whether the pedestrians are crossing the gate based on the behavior states;

[0029] If the pedestrian has not crossed the gate, jump to the step of identifying the behavior status corresponding to each pedestrian;

[0030] If the pedestrian is crossing the gate, an operation of determining whether the pedestrian's behavior is a tailgating behavior is triggered.

[0031] In a second aspect, the present application provides a gate channel tailgating behavior monitoring device, which is applied to a server configured with a target tailgating behavior recognition model, wherein the target tailgating behavior recognition model is a model obtained by training based on an artificial intelligence model; wherein the device includes:

[0032] A trajectory tracking module is used to establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, track the pedestrians to obtain real-time trajectories, and then save each target node and corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm;

[0033] a state recognition module, configured to recognize the behavior state corresponding to each of the pedestrians and determine whether the obtained behavior state of the pedestrian satisfies a preset condition; if the behavior state satisfies the preset condition, then the corresponding node identifier is removed from the preset relationship network to stop state recognition of the pedestrian corresponding to the corresponding target node;

[0034] The trajectory transmission module is used to determine that the pedestrian is currently following the vehicle if the behavior status does not meet the preset conditions, and transmit the real-time trajectory and pedestrian image corresponding to the pedestrian to a preset terminal device to initiate emergency response measures for the following behavior.

[0035] In a third aspect, the present application provides an electronic device, comprising:

[0036] Memory, used to store computer programs;

[0037] A processor is used to execute the computer program to implement the aforementioned gate channel tailgating behavior monitoring method.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned gate channel tailgating behavior monitoring method.

[0039] As can be seen from the above, before performing tailgating behavior recognition, the present application needs to establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and track the trajectory of the pedestrian to obtain a real-time trajectory, and then save each target node and the corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm; the behavior status corresponding to each pedestrian is identified, and it is determined whether the obtained behavior status of the pedestrian meets the preset conditions. If the behavior status meets the preset conditions, the corresponding node identifier is eliminated in the preset relationship network to stop the status recognition of the pedestrian corresponding to the corresponding target node; if the behavior status does not meet the preset conditions, it is determined that the current pedestrian has a tailing behavior, and the real-time trajectory corresponding to the pedestrian and the pedestrian image are transmitted to the preset terminal device to initiate emergency response measures for the tailing behavior.

[0040] It can be seen that the present application establishes target nodes and node identifiers corresponding to each pedestrian in the video image corresponding to the area to be identified, and tracks the pedestrians to obtain real-time trajectories. Then, each target node and corresponding node identifier are saved to a preset relationship network to identify the states corresponding to each pedestrian, so as to judge whether the pedestrian meets the preset legal conditions based on the obtained several behavioral states. If the behavioral state meets the preset legal conditions, the node identifier is eliminated in the preset relationship network to stop the state identification of the pedestrian corresponding to the target node corresponding to the node identifier; if the behavioral state corresponding to the pedestrian does not meet the preset legal conditions, the real-time trajectory corresponding to the pedestrian and the pedestrian image are transmitted to a preset terminal device so that relevant personnel can control the pedestrian. In this way, the accuracy of tailing behavior identification is improved, the trajectory of pedestrians with tailing behavior is tracked, the efficiency of controlling pedestrians with tailing behavior is improved, and the user experience is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of a method for monitoring tailgating behavior in gate channels disclosed in this application;

[0043] Figure 2 A schematic diagram of a flow chart for dividing and operating an area to be identified disclosed in this application;

[0044] Figure 3 This is a schematic diagram of a specific overall architecture for pedestrian recognition disclosed in this application;

[0045] Figure 4 This is a schematic diagram of a specific process of performing corresponding operations based on whether a pedestrian has crossed a gate disclosed in this application;

[0046] Figure 5 This is a schematic diagram of a specific result of pedestrian behavior analysis disclosed in this application;

[0047] Figure 6 A flowchart of pedestrian behavior detection disclosed in this application;

[0048] Figure 7 This is a specific flowchart of pedestrian behavior detection disclosed in this application;

[0049] Figure 8 A schematic diagram of the result of identifying sample data disclosed in this application;

[0050] Figure 9 This is a schematic diagram of pedestrian recognition in different situations disclosed in this application;

[0051] Figure 10 This is a schematic diagram of a specific tailgating behavior disclosed in this application;

[0052] Figure 11 This is a specific flow chart of gate channel tailgating behavior monitoring disclosed in this application;

[0053] Figure 12 This is a schematic structural diagram of a gate channel tailgating behavior monitoring device disclosed in this application;

[0054] Figure 13 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] At present, among the methods used to determine whether the behavior of pedestrians crossing the gate is legal, the infrared anti-tailing method is easily affected by the environment, sensitive to obstacles, lacks effective notification means when tailing behavior is detected, and requires frequent maintenance. The face recognition anti-tailing method cannot accurately identify individuals under special circumstances, lacks effective notification means when detecting tailing behavior, has low efficiency in personnel passage, and is integrated inside the gate. Subsequent upgrades are time-consuming and costly. The traditional physical three-roller gate anti-tailing method has a slow passage speed, is inconvenient for pedestrians to carry luggage, cannot perform character recognition and tracking, has high maintenance costs, and is not friendly to people with limited mobility. The AB door anti-tailing method requires a large space and has low pedestrian passage efficiency. To this end, the present application provides a gate channel tailing behavior monitoring method that can improve the accuracy of tailing behavior identification, track the trajectory of pedestrians with tailing behavior, and improve the efficiency of controlling pedestrians with tailing behavior.

[0057] See also Figure 1 As shown, an embodiment of the present invention discloses a method for monitoring tailgating behavior in a gate channel, which is applied to a server configured with a target tailgating behavior recognition model, wherein the target tailgating behavior recognition model is a model obtained by training based on an artificial intelligence model; wherein the method includes:

[0058] Step S11: establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, track the pedestrians to obtain real-time trajectories, and then save each target node and corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm.

[0059] Currently, in areas with high pedestrian traffic, such as train stations or subway stations, pedestrian traffic is relatively dense, making it difficult to identify the corresponding behavioral states of pedestrians. That is, in scenarios with dense pedestrian traffic and high resolution, existing algorithms have high difficulty in tracking target trajectories due to the high mobility of people and the large number of targets in front of the gates. This can easily lead to misjudgments, resulting in insufficient recognition accuracy in local areas.

[0060] In this embodiment, before establishing target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, the embodiment of the present application needs to divide the area to be identified in the real-time video to be identified, and use a preset resolution enhancement algorithm to process the video images to be processed corresponding to the obtained several areas to be processed. Specifically, establishing target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and tracking the trajectory of the pedestrian to obtain a real-time trajectory, can include: dividing the area to be identified in the real-time video to be identified using a preset area division rule to obtain the area to be processed, and using a preset resolution enhancement algorithm to process the video image to be processed corresponding to the area to be processed to obtain the target video image; using several processes that can share information with each other to establish target nodes corresponding to each pedestrian in the target video image and node identifiers corresponding to the target nodes, and tracking the trajectory of the pedestrian to obtain a real-time trajectory.

[0061] In the process of tracking pedestrian trajectories to obtain real-time trajectories, it is first necessary to determine the positions of each pedestrian in the area to be identified, and then determine the real-time trajectories corresponding to the pedestrians based on the pedestrian positions. Specifically, tracking pedestrian trajectories to obtain real-time trajectories may include: obtaining real-time video to be identified, determining the positions of each pedestrian in the area to be identified in the real-time video to be identified, and determining the real-time trajectories corresponding to the pedestrians based on the positions of each pedestrian.

[0062] In this embodiment, in order to improve the accuracy of the AI ​​(Artificial Intelligence) tailgating behavior detection model in detecting pedestrian behavior, Figure 2 As shown, Figure 2 The figure is a flowchart of the division and operation of the identification area. That is, in order to enable the AI ​​tailing behavior detection model to perform high-precision target detection on the gate-related area, the embodiment of the present application divides the key areas separately, and uses a preset super-resolution algorithm and other technical means to improve the resolution of the divided areas, thereby improving the detection accuracy of pedestrian behavior. At the same time, in order to ensure the real-time feedback of information, the embodiment of the present application enables separate processes for independent tracking of different detection areas, and at the same time uses shared memory technology to ensure that different processes can quickly obtain each other's detection results, so that the detection results are aligned in real time, further improving the accuracy of pedestrian behavior detection in the image.

[0063] In order to regularize the target nodes corresponding to the pedestrians in the target video image, established by each process, and the node identifiers corresponding to the target nodes, the embodiment of the present application needs to share the information obtained after the execution of several processes that can share information with each other. Specifically, saving each target node and the corresponding node identifier to a preset relationship network can include: based on the information shared between the processes, aligning the target nodes corresponding to the pedestrians and the node identifiers corresponding to the target nodes in real time, so that the aligned target nodes and the node identifiers corresponding to the target nodes are stored in the preset relationship network.

[0064] Step S12: Identify the behavior status corresponding to each pedestrian and determine whether the obtained behavior status of the pedestrian meets the preset conditions. If the behavior status meets the preset conditions, eliminate the corresponding node identifier in the preset relationship network to stop status identification of the pedestrian corresponding to the corresponding target node.

[0065] To speed up management personnel's control of pedestrians based on tailgating behavior, after the AI ​​tailgating behavior detection model detects and analyzes the trajectories of all targets in the image, it is necessary to reorder all current trajectory results so that results with abnormal behavior can be transmitted to the terminal in a timely manner and an alarm information can be generated for warning; if there is no abnormal behavior, the next detection process will be entered.

[0066] In this embodiment, a plurality of independent processes in a target following behavior recognition model that can share information are used to identify pedestrian states to obtain corresponding behavior states of the pedestrians. A preset priority sorting algorithm is then used to sort the behavior states in descending order of priority. Specifically, the behavior states corresponding to each pedestrian are identified, and it is determined whether the obtained behavior states meet preset conditions. If the behavior states meet the preset conditions, the corresponding node identifier is removed from the preset relationship network to stop state recognition of the pedestrian corresponding to the corresponding target node. This process may include: identifying the behavior states corresponding to each pedestrian to obtain corresponding behavior states; determining whether the obtained behavior states meet the preset conditions to obtain judgment results corresponding to each pedestrian; sorting the judgment results in descending order of priority using a preset priority sorting algorithm to obtain sorted results; wherein the priority corresponding to judgment results indicating that the preset conditions are met is higher than the priority corresponding to judgment results indicating that the preset conditions are not met; and obtaining the judgment results from the sorted results in a forward-to-backward order.

[0067] In order to make high-precision judgments on the status of pedestrians, the embodiment of this application introduces target detection and target tracking algorithms, and at the same time conducts targeted modeling based on the characteristics of high-speed railway stations with dense crowds. The overall architecture for pedestrian recognition is as follows: Figure 3 shown.

[0068] The embodiment of the present application utilizes an AI tailing behavior detection model to perform high-precision detection on targets in the identification area, and to judge the behavior information corresponding to the current target: if the target has passed the gate and the behavior is legal, the target node corresponding to the target is eliminated in the preset relationship graph, and the id corresponding to the current target is archived; if the target has not passed the gate, the current id is included in the preset relationship network as an activated node, and the trajectory of this target is continuously tracked, and the target trajectory is predicted, and then the obtained real-time trajectory is stored in the database. Specifically, if the judgment result representation obtained from the sorting result meets the preset conditions, the corresponding node identifier is eliminated in the preset relationship network to stop the state identification operation of the pedestrian corresponding to the corresponding target node; accordingly, if the behavior status does not meet the preset conditions, it is determined that the current pedestrian has tailing behavior, which may include: if the judgment result representation obtained from the sorting result does not meet the preset conditions, it is determined that the current pedestrian has tailing behavior.

[0069] In this embodiment, after identifying the behavior state corresponding to the pedestrian, it is necessary to determine whether the obtained behavior state meets the preset conditions. Specifically, identifying the behavior state corresponding to each pedestrian and determining whether the obtained behavior state of the pedestrian meets the preset conditions may include: identifying the behavior state corresponding to each pedestrian and determining whether the behavior of the pedestrian crossing the gate is a tailgating behavior based on the behavior state. Figure 4 As shown, Figure 4 This is a flowchart illustrating how to perform corresponding operations based on whether a pedestrian has crossed a gate. Once the AI ​​tailgating behavior detection model has located all targets in the current image, it is necessary to determine the status of the located targets: if the target has already crossed the gate, the node identifier corresponding to the current target is eliminated so that the status of the pedestrian corresponding to the target node corresponding to the node identifier is no longer identified in subsequent processes to reduce the error rate; if the target has not yet crossed the gate, the trajectory corresponding to the current target node is stored in the database and continuously tracked, and the target node is added to a preset relationship graph. Specifically, the behavior status corresponding to each pedestrian is identified, and based on the behavior status, whether the pedestrian's behavior of crossing the gate is tailgating behavior can include: identifying the behavior status corresponding to each pedestrian and determining whether the pedestrian is crossing the gate based on the behavior status; if the pedestrian has not crossed the gate, jumping to the step of identifying the behavior status corresponding to each pedestrian; if the pedestrian is crossing the gate, triggering the operation of determining whether the pedestrian's behavior is tailgating behavior.

[0070] In order to improve the accuracy of state judgment and trajectory tracking, the embodiment of the present application chooses to use a preset graph algorithm to construct a preset relationship graph, so that the AI ​​tailing behavior detection model can make a better judgment on whether the target has crossed the gate or whether there is any illegal tracking behavior.

[0071] Step S13: If the behavior status does not meet the preset conditions, it is determined that the pedestrian is currently following the vehicle, and the real-time trajectory and pedestrian image corresponding to the pedestrian are transmitted to a preset terminal device to initiate emergency response measures for the following behavior.

[0072] In this embodiment, the present application embodiment chooses to deploy computer vision algorithms into the native gate system of the high-speed railway station to identify and alert pedestrians who violate regulations by tracking and breaking into the gate, and assist on-site staff in efficiently managing entrances and exits. A schematic diagram of the results of pedestrian behavior analysis is shown in FIG. Figure 5 As shown in the figure, the pedestrian in the black thick line frame in the upper right corner and with invasion displayed above his head is the pedestrian who is following.

[0073] In this embodiment, the process of detecting pedestrian behavior is as follows: Figure 6 As shown: In places where security and anti-terrorism needs need to be checked, cameras are deployed to collect images of pedestrians in the scene, and the collected pedestrian images are pre-processed. The pre-processed pedestrian images are uploaded to a server equipped with an AI tailing detection model, and the AI ​​tailing detection model identifies and processes the pedestrian images. When the AI ​​tailing detection model finds that the pedestrian in the pedestrian image is following a person, it will send a signal to the alarm, mobile phone, tablet, duty room computer and other terminal devices to notify the on-site management personnel to reach the current location of the following person in time based on the signal received by the terminal device and control the following person. At the same time, the AI ​​tailing detection model will also start tracking the trajectory of the following person immediately after identifying the following behavior, and record the entire movement trajectory of the following person through image guidance to help management personnel control the following person.

[0074] The specific process of pedestrian behavior detection is as follows: Figure 7As shown: When cameras are deployed at train stations, they capture images from the entrance gate to the security checkpoint and then upload them to a server. An AI tailgating detection model installed on the server detects pedestrians in the images. If someone follows someone who has already passed the gate authentication into the station, the AI ​​tailgating detection model on the server detects this behavior, marks the target information, and issues a command, causing the alarm on the gate where the tailgating occurred to sound. This also sends an alarm message to on-site management personnel and displays an image of the person tailgating on their phone or tablet, assisting them in identifying and controlling the tailgating individual.

[0075] In this embodiment, when multiple tailgating incidents occur simultaneously, making it difficult for on-site managers to intervene promptly, the AI ​​trajectory tracking function within the AI ​​tailgating detection model not only marks the target but also records its movement trajectory and provides its current location in real time, allowing on-site managers to control the tailgating individuals according to the real-time trajectory prompts displayed on the terminal. In one specific embodiment, the target's movement trajectory may indicate that the target has boarded an escalator, exited the escalator, walked to the security checkpoint, passed security, and is seated in the fifth chair in the third row on the left side of the A1B1 waiting area.

[0076] In order to achieve high-precision detection of pedestrian behavior information and track pedestrian trajectories, the embodiment of the present application selected 102 targets in the sample video and annotated 20 samples for each target. The AI ​​tailing behavior detection model was used to detect the behavior of each target, thereby obtaining 102 trajectories. Among them, 92 trajectories corresponded to targets that were compliant pedestrian targets, and 10 trajectories corresponded to targets that were not compliant pedestrian targets. The specific results are as follows: Figure 8 shown.

[0077] Among the non-compliant pedestrian targets, 6 groups were simulated by staff members and 4 groups were real pedestrians. After being trained with 2,100 samples, the AI ​​tailing behavior detection model was tested in real scenes. When detecting 10 non-compliant pedestrian targets, 7 groups of non-compliant pedestrian targets were identified and detected. The specific results are as follows: Figure 9 It is worth mentioning that the schematic diagram of 3 non-compliant pedestrian targets among the 10 non-compliant pedestrian targets is as follows Figure 10 shown.

[0078] In addition, the AI ​​tailing behavior detection model includes a video input module, a self-built model target detection module, a detection result filtering module, a target allocation module, a target tracking module, an object recognition module, and a data output module, and the schematic diagram is as follows: Figure 11 As shown:

[0079] First, the function of the video input module is to perform frame extraction on the video to be identified and generate a timestamp using a timestamp generator.

[0080] Secondly, the self-built model object detection module performs image preprocessing on video frames, extracts features from video frames using a feature extraction network, performs region proposal network (RPN), bounding box regression and classification, and detection result filtering. Image preprocessing on video frames includes resizing, normalization, and data augmentation; feature extraction from video frames using the feature extraction network includes convolutional layers, residual blocks, and a feature fusion module; the region proposal network includes a sliding window generator, an anchor box regressor, and a classifier; and bounding box regression and classification includes bounding box regression, softmax classification, and confidence scoring. Detection result filtering includes non-maximum suppression (NMS) and filter.

[0081] Furthermore, the target assignment module includes the Hungarian Algorithm and the distance measurement.

[0082] Then, the target tracking module includes Kalman filter, state prediction, observation update and error covariance calculation.

[0083] Subsequently, the object recognition module includes feature extraction, classifier, and object-human association.

[0084] Finally, the data output module includes data storage, real-time display, and behavior analysis interface.

[0085] As can be seen from the above, before the embodiment of the present application monitors the tailgating behavior in the gate channel, it is necessary to establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and track the pedestrians to obtain real-time trajectories, and then save each target node and the corresponding node identifier to a preset relationship network to identify the states corresponding to each pedestrian, so as to judge whether the pedestrian meets the preset legal conditions based on the obtained several behavior states. If the behavior state meets the preset legal conditions, the node identifier is eliminated in the preset relationship network to stop the state identification of the pedestrian corresponding to the target node corresponding to the node identifier; if the behavior state corresponding to the pedestrian does not meet the preset legal conditions, the real-time trajectory corresponding to the pedestrian and the pedestrian image are transmitted to the preset terminal device so that relevant personnel can control the pedestrian. In this way, the accuracy of tailgating behavior identification is improved, the trajectory of pedestrians with tailing behavior is tracked, and the efficiency of controlling pedestrians with tailing behavior is improved.

[0086] Accordingly, see Figure 12 As shown, the present application also provides a gate channel tailgating behavior monitoring device, which is applied to a server configured with a target tailgating behavior recognition model, wherein the target tailgating behavior recognition model is a model obtained by training based on an artificial intelligence model; wherein the device includes:

[0087] The trajectory tracking module 11 is used to establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and to track the pedestrians to obtain real-time trajectories, and then save the target nodes and corresponding node identifiers to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm;

[0088] a state recognition module 12 for identifying the behavior state corresponding to each of the pedestrians and determining whether the obtained behavior state of the pedestrian satisfies a preset condition; if the behavior state satisfies the preset condition, eliminating the corresponding node identifier in the preset relationship network to stop state recognition of the pedestrian corresponding to the corresponding target node;

[0089] The trajectory transmission module 13 is used to determine that the pedestrian is currently following the vehicle if the behavior status does not meet the preset conditions, and transmit the real-time trajectory and pedestrian image corresponding to the pedestrian to a preset terminal device to initiate emergency response measures for the following behavior.

[0090] As can be seen from the above, before monitoring the tailing behavior in the gate channel, the embodiment of the present application needs to establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and track the trajectory of the pedestrian to obtain a real-time trajectory, and then save each target node and the corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm; the behavior status corresponding to each pedestrian is identified, and it is determined whether the obtained behavior status of the pedestrian meets the preset conditions. If the behavior status meets the preset conditions, the corresponding node identifier is eliminated in the preset relationship network to stop the status identification of the pedestrian corresponding to the corresponding target node; if the behavior status does not meet the preset conditions, it is determined that the current pedestrian has a tailing behavior, and the real-time trajectory corresponding to the pedestrian and the pedestrian image are transmitted to a preset terminal device to initiate emergency response measures for the tailing behavior.

[0091] It can be seen that the embodiment of the present application establishes target nodes and node identifiers corresponding to each pedestrian in the video image corresponding to the area to be identified, and tracks the pedestrian's trajectory to obtain a real-time trajectory. Then, each target node and the corresponding node identifier are saved to a preset relationship network to identify the state corresponding to each pedestrian, so as to determine whether the pedestrian meets the preset legal conditions based on the obtained several behavioral states. If the behavioral state meets the preset legal conditions, the node identifier is eliminated in the preset relationship network to stop the state identification of the pedestrian corresponding to the target node corresponding to the node identifier; if the behavioral state corresponding to the pedestrian does not meet the preset legal conditions, the real-time trajectory corresponding to the pedestrian and the pedestrian image are transmitted to a preset terminal device so that relevant personnel can control the pedestrian. In this way, the accuracy of tailing behavior identification is improved, the trajectory of pedestrians with tailing behavior is tracked, and the efficiency of controlling pedestrians with tailing behavior is improved.

[0092] In some specific implementations, the state recognition module 11 may specifically include:

[0093] A region division unit is used to divide the to-be-identified region in the real-time to-be-identified video using a preset region division rule to obtain a to-be-processed region, and to process the to-be-processed video image corresponding to the to-be-processed region using a preset resolution enhancement algorithm to obtain a target video image;

[0094] The trajectory tracking unit is used to establish target nodes corresponding to each pedestrian in the target video image and node identifiers corresponding to the target nodes by using several processes that can share information with each other, and to track the pedestrian to obtain a real-time trajectory.

[0095] In some specific implementations, the state recognition module 11 may specifically include:

[0096] An information alignment unit is used to align the target nodes corresponding to each of the pedestrians and the node identifiers corresponding to the target nodes in real time based on the information shared between the processes, so as to store the target nodes and the node identifiers corresponding to the target nodes that have been aligned in real time into the preset relationship network.

[0097] In some specific implementations, the state recognition module 11 may specifically include:

[0098] A video acquisition unit, configured to acquire a real-time video to be identified and determine a pedestrian position corresponding to each pedestrian in the area to be identified in the real-time video to be identified;

[0099] A real-time trajectory determination unit is used to determine the real-time trajectory corresponding to the pedestrian based on the position of each pedestrian.

[0100] In some specific implementations, the state recognition module 12 may specifically include:

[0101] a state recognition unit, configured to recognize the behavior states corresponding to the pedestrians to obtain the behavior states corresponding to the pedestrians;

[0102] a condition judgment unit, configured to judge whether the obtained behavior status of the pedestrians satisfies a preset condition, so as to obtain a judgment result corresponding to each of the pedestrians;

[0103] a result sorting unit, configured to sort the plurality of judgment results in descending order of priority using a preset priority sorting algorithm to obtain a sorting result; wherein the priority corresponding to the judgment result indicating that the preset condition is satisfied is higher than the priority corresponding to the judgment result indicating that the preset condition is not satisfied;

[0104] a judgment result obtaining unit, configured to obtain each judgment result from the sorting results in order from front to back;

[0105] an identification eliminating unit, configured to eliminate the corresponding node identification in the preset relationship network if the judgment result obtained from the sorting result indicates that the preset condition is satisfied, so as to stop the state identification operation for the pedestrian corresponding to the corresponding target node;

[0106] Accordingly, in some specific implementations, the state recognition module 12 may specifically include:

[0107] The first behavior judgment unit is configured to determine that the pedestrian currently has a tailgating behavior if the judgment result representation obtained from the sorting result does not satisfy the preset condition.

[0108] In some specific implementations, the state recognition module 12 may specifically include:

[0109] The second behavior judgment unit is used to identify the behavior status corresponding to each of the pedestrians, and judge whether the behavior of the pedestrian crossing the gate is a tailgating behavior based on the behavior status.

[0110] In some specific implementations, the state recognition module 12 may specifically include:

[0111] a third behavior judgment unit, configured to identify the behavior states corresponding to the pedestrians, and judge whether the pedestrians are crossing the gate based on the behavior states;

[0112] a step jump unit, configured to jump to the step of identifying the behavior states corresponding to the pedestrians if the pedestrians have not crossed the gate;

[0113] The fourth behavior judgment unit is used to trigger an operation of judging whether the pedestrian's behavior is a tailgating behavior if the pedestrian is crossing the gate.

[0114] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 13 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be considered as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the gate channel tailgating behavior monitoring method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0115] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0116] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0117] The operating system 221 is used to manage and control the hardware devices on the electronic device 20 and the computer program 222. It can be Windows Server, NetWare, Unix, Linux, etc. In addition to including a computer program capable of implementing the gate channel tailgating behavior monitoring method executed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs capable of performing other specific tasks.

[0118] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned disclosed method for monitoring tailgating behavior in a gate channel. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0120] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0122] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0123] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for monitoring tailgating behavior in a gate channel, characterized in that: The method is applied to a server configured with a target following behavior recognition model, wherein the target following behavior recognition model is a model obtained by training based on an artificial intelligence model; wherein the method includes: Establishing target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, tracking the pedestrian's trajectory to obtain a real-time trajectory, and then saving each target node and corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm; Identifying the behavior status corresponding to each of the pedestrians, and determining whether the obtained behavior status of the pedestrian meets a preset condition; if the behavior status meets the preset condition, eliminating the corresponding node identifier in the preset relationship network to stop status identification of the pedestrian corresponding to the corresponding target node; If the behavior status does not meet the preset conditions, it is determined that the pedestrian is currently following the vehicle, and the real-time trajectory and pedestrian image corresponding to the pedestrian are transmitted to a preset terminal device to initiate emergency response measures for the following behavior; Among them, the step of identifying the behavior status corresponding to each of the pedestrians and judging whether the obtained behavior status of the pedestrians meets the preset conditions includes: identifying the behavior status corresponding to each of the pedestrians, and judging whether the behavior of the pedestrian crossing the gate is a following behavior based on the behavior status; specifically: identifying the behavior status corresponding to each of the pedestrians, and judging whether the pedestrian is crossing the gate based on each of the behavior status; if the pedestrian has not crossed the gate, jumping to the step of identifying the behavior status corresponding to each of the pedestrians; if the pedestrian is crossing the gate, triggering the operation of judging whether the pedestrian's behavior is a following behavior.

2. The gate channel tailgating behavior monitoring method according to claim 1 is characterized in that: The step of establishing target nodes corresponding to respective pedestrians in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, and tracking the pedestrians to obtain real-time trajectories, includes: Dividing the to-be-identified area in the real-time to-be-identified video using a preset area division rule to obtain an to-be-processed area, and processing the to-be-processed video image corresponding to the to-be-processed area using a preset resolution enhancement algorithm to obtain a target video image; Target nodes corresponding to the pedestrians in the target video image and node identifiers corresponding to the target nodes are established by using several processes that can share information with each other, and the pedestrians are tracked to obtain real-time trajectories.

3. The gate channel tailgating behavior monitoring method according to claim 2 is characterized in that: The step of saving each target node and the corresponding node identifier to a preset relationship network includes: Based on the information shared between the processes, the target nodes corresponding to each of the pedestrians and the node identifiers corresponding to the target nodes are aligned in real time, so that the target nodes and the node identifiers corresponding to the target nodes that have been aligned in real time are stored in the preset relationship network.

4. The gate channel tailgating behavior monitoring method according to claim 1 is characterized in that: Tracking the pedestrian to obtain a real-time trajectory includes: Acquire a real-time video to be identified, and determine a pedestrian position corresponding to each pedestrian in the area to be identified in the real-time video to be identified; A real-time trajectory corresponding to each pedestrian is determined based on the position of each pedestrian.

5. The gate channel tailgating behavior monitoring method according to claim 1 is characterized in that: The identifying of the behavior status corresponding to each of the pedestrians and determining whether the obtained behavior status of the pedestrian meets a preset condition, and if the behavior status meets the preset condition, eliminating the corresponding node identifier in the preset relationship network to stop status identification of the pedestrian corresponding to the corresponding target node, includes: Identifying the behavior states corresponding to the pedestrians to obtain the behavior states corresponding to the pedestrians; Determining whether the obtained behavior status of the pedestrians meets a preset condition, so as to obtain a judgment result corresponding to each of the pedestrians; Sorting the plurality of judgment results in descending order of priority using a preset priority sorting algorithm to obtain a sorting result; wherein the priority corresponding to the judgment result indicating that the preset condition is satisfied is higher than the priority corresponding to the judgment result indicating that the preset condition is not satisfied; Obtaining each of the judgment results from the sorting results in order from front to back; If the judgment result obtained from the sorting result indicates that the preset condition is satisfied, the corresponding node identifier is eliminated from the preset relationship network to stop the state recognition operation for the pedestrian corresponding to the corresponding target node; Correspondingly, if the behavior state does not meet the preset conditions, it is determined that the current pedestrian is in a tailgating behavior, including: If the judgment result obtained from the sorting result indicates that the preset condition is not satisfied, it is determined that the pedestrian is currently following.

6. A gate channel tailgating behavior monitoring device, characterized in that: Applicable to a server configured with a target following behavior recognition model, wherein the target following behavior recognition model is a model obtained by training based on an artificial intelligence model; wherein the device includes: A trajectory tracking module is used to establish target nodes corresponding to each pedestrian in the video image corresponding to the area to be identified and node identifiers corresponding to the target nodes, track the pedestrians to obtain real-time trajectories, and then save each target node and corresponding node identifier to a preset relationship network; the preset relationship network is a relationship network established based on a preset graph algorithm; a state recognition module, configured to recognize the behavior state corresponding to each of the pedestrians and determine whether the obtained behavior state of the pedestrian satisfies a preset condition; if the behavior state satisfies the preset condition, then the corresponding node identifier is removed from the preset relationship network to stop state recognition of the pedestrian corresponding to the corresponding target node; a trajectory transmission module, configured to determine that the pedestrian is currently following a vehicle if the behavior status does not meet a preset condition, and transmit the real-time trajectory and pedestrian image corresponding to the pedestrian to a preset terminal device, so as to initiate emergency response measures for the following behavior; Among them, the gate channel tailgating behavior monitoring device is also used to identify the behavior status corresponding to each of the pedestrians, and judge whether the obtained behavior status of the pedestrians meets the preset conditions, including: identifying the behavior status corresponding to each of the pedestrians, and judging whether the behavior of the pedestrian crossing the gate is a tailgating behavior based on the behavior status; specifically: identifying the behavior status corresponding to each of the pedestrians, and judging whether the pedestrian is crossing the gate based on each of the behavior status; if the pedestrian has not crossed the gate, jumping to the step of identifying the behavior status corresponding to each of the pedestrians; if the pedestrian is crossing the gate, triggering the operation of judging whether the pedestrian's behavior is a tailgating behavior.

7. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the gate channel tailgating behavior monitoring method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, the steps of the gate channel tailgating behavior monitoring method as described in any one of claims 1 to 5 are implemented.

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