Intrusion detection method and apparatus, and storage medium and electronic device

Abstract

Disclosed in the present application are an intrusion detection method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring a target video frame to be subjected to detection; performing detection on the target video frame by means of a target detection module in a target detection model, so as to obtain a first detection result; when the first detection result indicates that there is image information of a target object in the target video frame, performing detection on the target video frame by means of a target key point detection module in the target detection model, so as to obtain key point coordinate information of the target object; and on the basis of the key point coordinate information and first coordinate information corresponding to a target area, detecting whether there is an area intrusion in the target video frame, so as to obtain a second detection result. The present application solves the technical problem in the related art of the accuracy of determination on whether there is an intrusion being low due to the determination on whether there is a human intrusion being performed by means of a positional relationship between a center point of a target object detection box and an intrusion area.

Description

[0001]This application claims priority to Chinese patent application number 202410205991.0, filed with the China Patent Office on February 23, 2024, entitled "Intrusion Detection Method and Apparatus, Storage Medium, and Electronic Device," the entire contents of which are incorporated herein by reference. Technical Field: This application relates to the field of artificial intelligence technology, and more specifically, to an intrusion detection method and apparatus, a storage medium, and an electronic device. Background: With the rapid development of big data, to ensure data security and avoid physical security risks, certain vulnerable areas, sensitive areas, or objects require real-time detection of human intrusion using regional intrusion algorithms. This allows for timely identification and resolution of risks, maximizing asset security. Conventional regional intrusion algorithms determine human intrusion based on the positional relationship between the center point of a target object detection frame and the intrusion area. This detection method suffers from relatively poor accuracy and is prone to missed and false alarms due to factors such as the large target object detection frame and the viewing angle. Currently, no effective solution has been proposed to the problem that, in the aforementioned related art, the accuracy of determining whether a person has intruded is relatively low, as determined by the positional relationship between the center point of a target object detection frame and the intrusion area. SUMMARY OF THE INVENTION The present application provides an intrusion detection method and apparatus, a storage medium, and an electronic device to at least address the technical problem in the related art of determining whether a person has intruded, as determined by the positional relationship between the center point of a target object detection frame and the intrusion area. According to one aspect of an embodiment of the present application, an intrusion detection method is provided, comprising: obtaining a target video frame to be detected, wherein the target video frame includes at least an image of a target area; detecting the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is configured to characterize whether image information of the target object exists in the target video frame; if the first detection result characterizes the presence of image information of the target object in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; and detecting whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result.Furthermore, before detecting the target video frame using the target detection module in the target detection model to obtain the first detection result, the method further includes: selecting a target region in the target video frame using a rotating frame detection module in the target detection model to obtain multiple first initial frames and a first confidence level corresponding to a first initial frame among the multiple first initial frames, wherein the rotating frame detection module is configured to detect the target region in the target video frame, and the first confidence level indicates the accuracy of the first initial frames; determining a first target frame from the multiple first initial frames based on the first confidence level; and determining first coordinate information corresponding to the target region based on the first target frame. Furthermore, detecting whether there is a region intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target region to obtain the second detection result includes: determining a positional relationship between the key point coordinate information and the first coordinate information; and detecting whether there is a region intrusion in the target video frame based on the positional relationship and a preset detection strategy to obtain the second detection result. Furthermore, after detecting whether a region intrusion occurs in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target region and obtaining a second detection result, the method further includes: determining a region intrusion type based on the positional relationship; determining a region risk level based on the region type corresponding to the target region; and determining a processing strategy based on the region intrusion type and the region risk level, wherein the region intrusion event is processed based on the processing strategy. Furthermore, after detecting the target video frame using an object detection module in an object detection model and obtaining a first detection result, the method further includes: if the first detection result indicates that image information of the target object is absent in the target video frame, selecting a target region in the target video frame using a rotating frame detection module in the object detection model based on a preset time period to obtain multiple second initial frames and second confidence levels corresponding to the second initial frames in the multiple second initial frames; determining a second target frame from the multiple second initial frames based on the second confidence level; and determining second coordinate information corresponding to the target region based on the second target frame. Furthermore, after determining the second coordinate information corresponding to the target area based on the second target frame, the method also includes: determining whether the second coordinate information is the same as the first coordinate information; if the second coordinate information is not the same as the first coordinate information, updating the second coordinate information to the first coordinate information.Furthermore, before detecting the target video frame using the target key point detection module in the target detection model to obtain key point coordinate information of the target object, the method further includes: acquiring training video frames and preprocessing the training video frames to obtain processed training video frames; constructing a training set based on the processed training video frames and the real labels corresponding to the processed training video frames to obtain a target training set, wherein the real labels are the real coordinate information of the detection box of the target object, the real coordinate information of the target area, or the real coordinate information of the key points of the target object; and training the initial detection module, the initial rotation box detection module, and the initial key point detection module based on the target training set to obtain the target detection model. According to another aspect of an embodiment of the present application, an intrusion detection method is provided, including: obtaining a target video frame to be detected uploaded by a client, wherein the target video frame includes at least an image of a target area; detecting the target video frame using a target detection module in a target detection model on a cloud server to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is configured to indicate whether image information of the target object exists in the target video frame; if the first detection result indicates that image information of the target object exists in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain coordinate information of key points of the target object, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; detecting whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result; and returning the second detection result to the client.According to another aspect of an embodiment of the present application, an intrusion detection device is further provided, comprising: an acquisition unit, configured to acquire a target video frame to be detected, wherein the target video frame includes at least an image of a target area; a first detection unit, configured to detect the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is used to characterize whether image information of the target object exists in the target video frame; a second detection unit, configured to detect the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object when the first detection result characterizes the presence of image information of the target object in the target video frame, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; and a third detection unit, configured to detect whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result. Furthermore, the apparatus further includes: a first frame selection unit configured to, before detecting the target video frame using the target detection module in the target detection model and obtaining a first detection result, select a target region in the target video frame using a rotation frame detection module in the target detection model to obtain a plurality of first initial frames and a first confidence level corresponding to a first initial frame among the plurality of first initial frames, wherein the rotation frame detection module is configured to detect the target region in the target video frame, and the first confidence level is used to indicate the accuracy of the first initial frames; a first determination unit configured to determine a first target frame from the plurality of first initial frames based on the first confidence level; and a second determination unit configured to determine first coordinate information corresponding to the target region based on the first target frame. Furthermore, the third detection unit includes: a judgment module configured to determine a positional relationship between the key point coordinate information and the first coordinate information; and a determination module configured to detect whether there is a region intrusion in the target video frame based on the positional relationship and a preset detection strategy, thereby obtaining a second detection result.Furthermore, the device also includes: a third determination unit, configured to detect whether there is regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area, and after obtaining a second detection result, determine the type of regional intrusion based on the positional relationship; a fourth determination unit, configured to determine the regional risk level based on the regional type corresponding to the target area; and a fifth determination unit, configured to determine a processing strategy based on the regional intrusion type and the regional risk level, wherein the regional intrusion event is processed according to the processing strategy. Furthermore, the apparatus further includes: a second frame selection unit configured to, after detecting the target video frame using the target detection module in the target detection model and obtaining a first detection result, select a target region in the target video frame using the rotating frame detection module in the target detection model according to a preset time period, if the first detection result indicates that image information of the target object is absent in the target video frame, to obtain a plurality of second initial frames and a second confidence level corresponding to a second initial frame in the plurality of second initial frames; a sixth determination unit configured to determine a second target frame from the plurality of second initial frames based on the second confidence level; and a seventh determination unit configured to determine second coordinate information corresponding to the target region based on the second target frame. Furthermore, the apparatus further includes: a judgment unit configured to, after determining second coordinate information corresponding to the target region based on the second target frame, determine whether the second coordinate information is identical to the first coordinate information; and an updating unit configured to update the second coordinate information to the first coordinate information if the second coordinate information is different from the first coordinate information. Furthermore, the device also includes: an acquisition unit, configured to acquire training video frames and pre-process the training video frames to obtain processed training video frames before detecting the target video frames through the target key point detection module in the target detection model to obtain key point coordinate information of the target object; a construction unit, configured to construct a training set based on the processed training video frames and the real labels corresponding to the processed training video frames to obtain a target training set, wherein the real labels are the real coordinate information of the detection box of the target object, the real coordinate information of the target area, or the real coordinate information of the key points of the target object; and a training unit, configured to train the initial detection module, the initial rotation box detection module, and the initial key point detection module based on the target training set to obtain the target detection model.According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided. The storage medium stores a program. When the program is executed, the device containing the storage medium is controlled to execute any one of the aforementioned intrusion detection methods. According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory storing an executable program and a processor configured to execute the program. When the program is executed, the device executes any one of the aforementioned intrusion detection methods. In an embodiment of the present application, the following steps are employed: obtaining a target video frame to be detected, wherein the target video frame includes at least an image of a target area; detecting the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is configured to indicate whether image information of the target object exists in the target video frame; if the first detection result indicates that image information of the target object exists in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain coordinate information of key points of the target object, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; and detecting whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area to obtain a second detection result. This solves the technical problem in related arts of determining whether a person has intruded based on the positional relationship between the center point of a target object detection frame and the intrusion area, resulting in relatively low accuracy in determining whether an intrusion exists. In this solution, the target detection module in the target detection model first determines whether image information of the target object exists in the target video frame. If the target object image information exists, the target key point detection module in the target detection model then detects the key points of the target object in the target video frame to obtain the key point coordinate information of the target object. Finally, based on the key point coordinate information and the first coordinate information corresponding to the target area, the target video frame is detected to determine whether there is a regional intrusion. Compared to the prior art method of determining whether a person has intruded based on the positional relationship between the center point of the target object detection frame and the intrusion area, detecting whether there is a regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information can effectively avoid the problems of missed alarms and false alarms caused by factors such as the large range of the target object detection frame and the shooting angle, thereby achieving the technical effect of improving the accuracy of determining whether there is an intrusion. BRIEF DESCRIPTION OF THE DRAWINGS The drawings described herein are provided to provide a further understanding of the present application and constitute a part of this application. The illustrative embodiments of this application and their description are provided to explain this application and do not constitute undue limitations thereon.In the accompanying drawings: Figure 1 is a schematic diagram of a computer terminal according to the first embodiment of the present application; Figure 2 is a flow chart of an intrusion detection method according to the first embodiment of the present application; Figure 3 is a schematic diagram of an intrusion detection method according to the first embodiment of the present application; Figure 4 is a flow chart of an intrusion detection method according to the second embodiment of the present application; Figure 5 is a schematic diagram of an intrusion detection device according to the third embodiment of the present application; and Figure 6 is a schematic diagram of a computer terminal according to the fourth embodiment of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS To help those skilled in the art better understand the present invention, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. It should be understood that the described embodiments are merely a portion of the embodiments of the present application, and are not intended to be exhaustive. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present application without inventive effort shall fall within the scope of protection of the present application. It should be noted that the terms "first," "second," and so on, in the specification and claims of the present application, and in the accompanying drawings, are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements need not be limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus. First, some nouns or terms appearing in the description of the embodiments of the present application are subject to the following interpretations: Intrusion zone: generally refers to a protected spatial range or the outer contour of a specific object. From the perspective of a detection algorithm, this spatial range or physical object can be outlined by a detection box. Rotated box detection: an algorithm used to detect rotated rectangular objects in an image. The detected object can be a rectangle rotated at any angle. Keypoint detection: a computer vision task aimed at detecting and locating key points of a posture in an image, such as the head, shoulders, elbows, knees, ankles, etc.It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, storage, and display, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. The collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant region, and corresponding operation portals are provided for the user to choose to authorize or refuse. Example 1 According to an embodiment of the present application, an intrusion detection method is also provided. It should be noted that the steps shown in the flowchart of the accompanying figure can be executed in a computer system such as a set of computer-executable instructions. Although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than shown. The method embodiment provided in Example 1 of the present application can be executed in a mobile terminal, computer terminal, or similar computing device. Figure 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing the intrusion detection method. As shown in FIG1 , a computer terminal (or mobile device) 10 may include a processor assembly 102 (processor assembly 102 may include, but is not limited to, a processing device such as a microprocessor (MCU) or a programmable logic device (FPGA), and processor assembly 102 may include a processor assembly, as shown in FIG1 by 102a, 102b, ..., 102n), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the computer terminal 100 may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS), a network interface, a power supply, and / or a camera. Those skilled in the art will appreciate that the structure shown in FIG1 is merely illustrative and does not limit the structure of the electronic device described above. For example, the computer terminal 10 may include more or fewer components than shown in FIG1 , or have a configuration different from that shown in FIG1 . It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any other component of the computer terminal 10 (or mobile device).As described in the embodiments of the present application, the data processing circuit acts as a processor control (for example, selecting a variable resistor terminal path connected to an interface). Memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the intrusion detection method in the embodiments of the present application. Processor 102 executes the software programs and modules stored in memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned intrusion detection method. Memory 104 may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located from processor 102, and these remote memories can be connected to computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. Transmission module 106 is used to receive or transmit data via a network. Specific examples of such networks may include a wireless network provided by the communications provider of computer terminal 10. In one example, the transmission module 106 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module for wireless communication with the Internet. The display can be, for example, a touch-screen liquid crystal display, which enables a user to interact with the user interface of the computer terminal 10 (or mobile device). In the above operating environment, the present application provides an intrusion detection method as shown in FIG2 . FIG2 is a flowchart of the intrusion detection method according to the first embodiment of the present application. The method includes: Step S201: Acquire a target video frame to be detected, wherein the target video frame includes at least an image of a target area. Optionally, a real-time video stream can be captured by a video capture device (such as a network camera), and then the real-time video stream is decoded to obtain the target video frame. It should be noted that the real-time video stream or target video frame includes at least an image of the intrusion area (i.e., the target area). The above-mentioned target video frame is used for detection to determine whether there is a person (ie, the target object) in the video screen.In step S202, the target video frame is detected by the target detection module in the target detection model to obtain a first detection result. The target detection module is configured to detect whether image information of the target object exists in the target video frame, and the first detection result indicates whether image information of the target object exists in the target video frame. Optionally, the first detection result is obtained by detecting whether image information of the target object exists in the target video frame by the target detection module in the target detection model. It should be noted that the target detection module may be a YOLO v8 model. If the first detection result indicates that image information of the target object does not exist in the target video frame, this indicates that there is no regional intrusion in the current target video frame, and the target detection module can be used to directly detect whether the target object exists in the next target video frame. In step S203, if the first detection result indicates that image information of the target object exists in the target video frame, the target key point detection module in the target detection model is configured to detect key points of the target object in the target video frame to obtain coordinate information of key points of the target object. Optionally, if the first detection result indicates the presence of image information of the target object in the target video frame, the target key point detection model in the target detection model is used to detect key points of the target object in the target video frame to obtain the aforementioned key point coordinate information. It should be noted that the aforementioned key points may be parts such as the head, shoulders, elbows, knees, ankles, and the aforementioned target key point detection model may be the HRNet-w32 model. In step S204, the target video frame is detected for regional intrusion based on the key point coordinate information and the first coordinate information corresponding to the target area, thereby obtaining a second detection result. Optionally, after obtaining the aforementioned key point coordinate information, the target video frame is detected for regional intrusion based on the relationship between the key point coordinate information and the first coordinate information corresponding to the target area to obtain the aforementioned second detection result. It should be noted that the second coordinate information corresponding to the target area may be obtained by manually annotating the target area.In summary, the target detection module in the target detection model first determines whether image information of the target object exists in the target video frame. If image information of the target object exists, the target key point detection module in the target detection model then detects key points of the target object in the target video frame to obtain coordinate information of the key points of the target object. Finally, based on the key point coordinate information and first coordinate information corresponding to the target area, it is detected whether there is a regional intrusion in the target video frame. Compared with the prior art method of determining whether there is a human intrusion based on the positional relationship between the center point of the target object detection frame and the intrusion area, detecting whether there is a regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information can effectively avoid the problems of missed alarms and false alarms caused by factors such as the large range of the target object detection frame and the shooting angle, thereby achieving the technical effect of improving the accuracy of determining whether there is an intrusion. To improve the accuracy of the coordinate information of the target area, in the intrusion detection method provided in the first embodiment of the present application, before the target detection module in the target detection model detects the target video frame and obtains the first detection result, the method further includes: selecting the target area in the target video frame using the rotating frame detection module in the target detection model to obtain multiple first initial frames and a first confidence level corresponding to the first initial frame in the multiple first initial frames. The rotating frame detection module is used to detect the target area in the target video frame, and the first confidence level is used to indicate the accuracy of the first initial frames; determining a first target frame from the multiple first initial frames based on the first confidence level; and determining the first coordinate information corresponding to the target area based on the first target frame. Optionally, before the target detection module in the target detection model detects the target video frame and obtains the first detection result, the rotating frame detection module in the target detection model may select the target area in the target video frame to obtain multiple first initial frames and a first confidence level corresponding to the first initial frame in the multiple first initial frames. It should be noted that the first confidence level is used to indicate the accuracy of the first initial frame. Then, a first target frame can be determined from the multiple first initial frames based on the first confidence level and the confidence threshold. Finally, first coordinate information corresponding to the target region can be determined based on the position information of the first target frame. It should be noted that the aforementioned confidence threshold can be set based on actual needs and is not specifically limited here. It should be noted that the aforementioned rotated frame detection module can utilize the PPYOLOE-R model.Determining the coordinate information of the target area using a rotating box detection model can effectively improve the efficiency of determining the intrusion area compared to the prior art method of determining the intrusion area through manual annotation detection. Compared to the prior art method of determining the intrusion area through horizontal box detection, this method can effectively avoid the problem of low accuracy of the intrusion area due to the large amount of redundant areas contained in the horizontal box, thereby achieving the technical effect of improving the accuracy of determining regional intrusion. To improve the accuracy of determining regional intrusion, in the intrusion detection method provided in Example 1 of the present application, detecting whether a regional intrusion exists in a target video frame based on key point coordinate information and first coordinate information corresponding to the target area, and obtaining a second detection result includes: determining a positional relationship between the key point coordinate information and the first coordinate information; and detecting whether a regional intrusion exists in the target video frame based on the positional relationship and a preset detection strategy to obtain the second detection result. Optionally, after obtaining the aforementioned key point coordinate information and the first coordinate information corresponding to the target area, determining whether a regional intrusion exists by determining the positional relationship between the key point coordinate information and the first coordinate information and a preset detection strategy. It should be noted that the preset detection strategy can be set based on actual needs. In an optional embodiment, the preset detection strategy may be to detect whether the key point coordinate information is within the area determined by the first coordinate information. If the key point coordinate information is within the area determined by the first coordinate information, it indicates that a portion of the target object has entered the target area. Therefore, the second detection result can be determined as the presence of an area intrusion in the target video frame. If the key point coordinate information is outside the area determined by the first coordinate information, it indicates that no portion of the target object has entered the target area. Therefore, the second detection result can be determined as the absence of an area intrusion in the target video frame. In an optional embodiment, the preset detection strategy may also be to set a distance threshold, and then determine whether an area intrusion has occurred based on the distance threshold and the aforementioned positional relationship. For example, the distance between the key point coordinate information and the area boundary determined by the first coordinate information is determined based on the aforementioned positional relationship. If the distance value is less than or equal to the distance threshold, the second detection result can be determined as the presence of an area intrusion in the target video frame. Correspondingly, if the distance value is greater than the distance threshold, the second detection result can be determined as the absence of an area intrusion in the target video frame. The key point coordinate information can effectively avoid the problems of missed alarms and false alarms caused by the large range of the target object detection frame and the shooting angle, thereby achieving the technical effect of improving the accuracy of intrusion detection.To further improve intrusion detection accuracy, in the intrusion detection method provided in Example 1 of the present application, after detecting whether a regional intrusion exists in a target video frame based on key point coordinate information and first coordinate information corresponding to a target region and obtaining a second detection result, the method further includes: determining a regional intrusion type based on the positional relationship; determining a regional risk level based on the region type corresponding to the target region; and determining a processing strategy based on the region intrusion type and the region risk level, wherein the regional intrusion event is processed based on the processing strategy. Optionally, after determining whether a regional intrusion exists in the target video frame, the region intrusion type is determined based on the positional relationship. For example, the region intrusion types may include: full body intrusion, leg intrusion, arm intrusion, or other body part intrusion. It should be noted that the region intrusion type can be set according to actual needs and is not limited here. To further refine the risk level of the regional intrusion, the aforementioned target region may correspond to different region types, with different risk levels for different region types. Finally, a processing strategy for the regional intrusion event is determined based on the region intrusion type and the region risk level. In an optional embodiment, the regional intrusion type and regional risk level can be used to determine the alert severity of the regional intrusion event, for example, severe, moderately severe, general, or non-severe. Then, based on the alert severity of the regional intrusion event, a handling strategy can be determined, for example, real-time handling, handling within an hour, handling within the same day, or handling within a week. In summary, by distinguishing specific intrusion types based on the relationship between key points and the location of the intrusion area, and thereby generating alerts of varying severity, this effectively implements refined alert operations. In actual applications, the target area may change. Once the acquisition point or on-site conditions change, manual re-labeling is required. Failure to label or update coordinates in a timely manner may result in incorrect intrusion area information and ineffective regional intrusion detection. To address the above issues, in the intrusion detection method provided in the first embodiment of the present application, after the target detection module in the target detection model detects the target video frame and obtains a first detection result, the method further includes: if the first detection result indicates that image information of the target object is absent in the target video frame, the rotating frame detection module in the target detection model selects the target area in the target video frame according to a preset time period to obtain multiple second initial frames and second confidence levels corresponding to the second initial frames in the multiple second initial frames; determining a second target frame from the multiple second initial frames based on the second confidence level; and determining second coordinate information corresponding to the target area based on the second target frame.In an optional embodiment, in the intrusion detection method provided in Example 1 of the present application, after determining the second coordinate information corresponding to the target area based on the second target frame, the method further includes: determining whether the second coordinate information is identical to the first coordinate information; if the second coordinate information is not identical to the first coordinate information, updating the second coordinate information to the first coordinate information. Optionally, after the target detection module in the target detection model detects the target video frame and obtains a first detection result, if the first detection result indicates that image information of the target object is absent in the target video frame, the rotating frame detection module in the target detection model can select the target area in the target video frame based on a preset time period to obtain multiple second initial frames and second confidence levels corresponding to the second initial frames. Based on the second confidence levels, a second target frame and coordinate information of the second target frame are determined from the multiple second initial frames, thereby obtaining second coordinate information corresponding to the target area. After obtaining the second coordinate information, it is further determined whether the second coordinate information is identical to the first coordinate information. If the second coordinate information is not identical to the first coordinate information, the second coordinate information is updated to the first coordinate information, thereby dynamically updating the position of the target area. By using the rotating frame detection module to select the target region's location information in target video frames without target object image information, this effectively avoids inaccurate target region location information due to occlusion of the target region by the target object. It also effectively addresses the need for manual re-labeling due to changes in acquisition points or field conditions, thereby achieving the technical effect of improving intrusion detection accuracy. Obtaining the aforementioned target detection model is crucial. Therefore, in the intrusion detection method provided in Example 1 of the present application, before using the target key point detection module in the target detection model to detect the target video frames and obtain the target object's key point coordinate information, the method further includes: acquiring training video frames and preprocessing the training video frames to obtain processed training video frames; constructing a training set based on the processed training video frames and the ground truth labels corresponding to the processed training video frames to obtain a target training set, where the ground truth labels are the ground truth coordinate information of the target object's detection frame, the target region's ground truth coordinate information, or the target object's key point coordinate information; and training the initial detection module, the initial rotating frame detection module, and the initial key point detection module based on the target training set to obtain a target detection model. Optionally, in the intrusion detection method provided in the first embodiment of the present application, the target detection model is trained by the following steps: First, a training video stream can be captured by a video capture device, and the video stream is decoded to obtain the training video frame.To improve the quality of the training video frames, the training video frames can be preprocessed, for example, by performing data cleaning, image processing, and data augmentation. After obtaining the processed training video frames, the corresponding true labels are determined based on the module being trained. For example, if the initial detection model is currently being trained, the corresponding true labels may include the following two cases: if the training video frame contains image information of the target object, the true label may be the coordinate information of the target object detection box; if the training video frame does not contain image information of the target object, the true label may be a null value. If the initial rotation box detection module is currently being trained, the corresponding true label may be the true coordinate information of the target region; if the initial key point detection module is currently being trained, the corresponding true label may be the true coordinate information of the target object's key points (for example, the coordinate information of the 17 key points of the human body). In an optional embodiment, if the initial rotation box detection module is currently being trained, the corresponding true label may also include the target region type, so that when the rotation box detection module detects the target region's coordinate information, the target region type can also be predicted. Then, a training set is constructed based on the processed training video frames and the true labels corresponding to the processed training video frames to obtain a target training set. Finally, the initial detection module, initial rotating box detection module, and initial key point detection module are trained based on the target training set to obtain the aforementioned target detection model. For example, the initial detection module is trained using the processed training video frames and the true labels of the coordinates of each key point of the target object; the initial rotating box detection module is trained using the processed training video frames and the true coordinate information of the target area and the target area type; and the initial key point detection module is trained using the processed training video frames and the true coordinate information of the key points of the target object. In an optional embodiment, FIG3 is a schematic diagram of intrusion detection provided according to the first embodiment of the present application. As shown in FIG3 , Step 1: Offline Generation of Algorithm Model Files: Offline generation of a high-performance executable algorithm model file includes a detection model (i.e., the aforementioned target detection module), a rotating box detection model (i.e., the aforementioned rotating box detection module), and a key point detection model (i.e., the aforementioned target key point detection module). The aforementioned detection model, rotating box detection model, and key point detection model can adopt deep learning algorithm models. The basic algorithm model can refer to or reuse currently popular models. For example, the detection model can adopt the YOLO v8 model, the rotating box detection model can adopt the PPYOLOE-R model, and the key point detection model can adopt the HRNet-w32 model.Generating the aforementioned detection model, rotating box detection model, and key point detection model primarily involves four steps: training data collection, image data processing, model training, model packaging, and deployment, ultimately resulting in an algorithm model file that can be executed on the terminal. Step 2: Define intrusion alert rules. Based on actual mission requirements and scenarios, define intrusion alert rules, including intrusion type, severity level, and alert handling requirements. These three correspond to each other and are used for alert generation and security operations. For example, intrusion types include full body intrusion, leg intrusion, arm intrusion, and other body parts intrusion; severity levels include severe, moderate, moderate, and non-severe; and alert handling requirements include real-time processing, processing within 1 hour, processing within the same day, and processing within 1 week. Step 3: Online regional intrusion detection: Video acquisition devices (such as network cameras) capture real-time video streams, decode them into monitoring video frames, and perform detection to determine whether a person is within the image. When no one is in the frame, a rotating frame periodically detects intrusion areas, promptly updating the coordinates of intrusion areas as collection points or field conditions change. The detection frequency is determined based on the actual task situation and computing power. Because people in the frame may obstruct the intrusion area, resulting in inaccurate detection, detection is performed when no one is in the frame. When people are in the frame, keypoint detection is performed to obtain the coordinates of all keypoints of the target object, including the head, shoulders, elbows, knees, ankles, etc. After each keypoint detection algorithm outputs a result, it determines whether an intrusion has occurred and the type of intrusion based on the positional relationship between the keypoints and the intrusion area. For example, an elbow keypoint in the intrusion area constitutes an arm intrusion, while thigh, knee, or calf keypoints in the intrusion area constitute a leg intrusion. If an area intrusion occurs, the program generates an alarm of the corresponding level on the security operations platform based on the intrusion type, which is then forwarded to on-site operations personnel for processing. The intrusion detection method provided in Example 1 of the present application uses a rotating frame detection module to detect the intrusion area. Key point coordinates are obtained using a key point detection algorithm. The presence and type of intrusion are determined based on the relationship between the key point coordinates and the intrusion area, and different levels of alerts are generated based on the intrusion type. Compared to conventional area-based intrusion detection methods, this intrusion detection method has the following advantages: First, the rotating frame detection algorithm dynamically detects the intrusion area, avoiding missed alerts and false alerts caused by untimely manual annotation of intrusion areas after changes in collection points or field conditions; second, it provides sufficient human posture information, reducing missed alerts and false alerts caused by missing human posture information; and third, it can distinguish specific intrusion types based on the relationship between different human key points and the intrusion area, thereby generating different levels of alerts and achieving refined alert operations.In the intrusion detection method provided in the first embodiment of the present application, a target video frame to be detected is obtained, wherein the target video frame includes at least an image of a target area; a target detection module in a target detection model is used to detect whether image information of a target object exists in the target video frame, and the first detection result is used to indicate whether image information of the target object exists in the target video frame; when the first detection result indicates that image information of the target object exists in the target video frame, the target video frame is detected by a target key point detection module in the target detection model to obtain coordinate information of key points of the target object, wherein the target key point detection module is used to detect key points of the target object in the target video frame; and a second detection result is obtained by detecting whether there is a regional intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area. This method solves the technical problem in related arts of determining whether a person has intruded based on the positional relationship between the center point of a target object detection frame and the intrusion area, resulting in relatively low accuracy in determining whether there is an intrusion. In this solution, the target detection module in the target detection model first determines whether image information of the target object exists in the target video frame. If image information of the target object exists, the target key point detection module in the target detection model then detects key points of the target object in the target video frame to obtain key point coordinate information of the target object. Finally, based on the key point coordinate information and first coordinate information corresponding to the target area, the target video frame is detected to determine whether there is a regional intrusion. Compared to the prior art method of determining whether a person has intruded based on the positional relationship between the center point of the target object detection frame and the intrusion area, detecting whether there is a regional intrusion in the target video frame based on key point coordinate information and first coordinate information effectively avoids missed alarms and false alarms caused by factors such as the large range of the target object detection frame and the shooting angle, thereby achieving the technical effect of improving the accuracy of determining whether there is an intrusion. It should be noted that for the sake of simplicity, the aforementioned method embodiments are described as a series of combined actions. However, those skilled in the art should understand that this application is not limited to the order of the actions described, as certain steps can be performed in a different order or simultaneously according to this application. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for this application. Through the above description of the embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software plus a required general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred implementation method.Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can essentially be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present application. Example 2 According to an embodiment of the present application, an intrusion detection method is further provided, as shown in Figure 4, the method includes: step S401, obtaining a target video frame to be detected uploaded by a client, wherein the target video frame includes at least an image of a target area; step S402, detecting the target video frame using a target detection module in a target detection model in a cloud server to obtain a first detection result, wherein the target detection module is used to detect whether image information of a target object exists in the target video frame, and the first detection result is used to characterize whether image information of the target object exists in the target video frame; when the first detection result characterizes the presence of image information of the target object in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object, wherein the target key point detection module is used to detect key points of the target object in the target video frame; detecting whether there is regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area, to obtain a second detection result; step S403, returning the second detection result to the client. In the cloud server, the specific method for determining whether a region intrusion exists in a target video frame is the same as that described in Example 1 and will not be further described here. It should be noted that, for simplicity, the aforementioned method embodiments are presented as a series of combined actions. However, those skilled in the art should be aware that this application is not limited to the order of the actions described, as certain steps may be performed in a different order or simultaneously. Furthermore, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for this application. Through the above description of the embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software and a necessary general-purpose hardware platform. Hardware can also be used, but in many cases the former is the preferred implementation.Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, can essentially be embodied in the form of a software product. This computer software product is stored in a storage medium (e.g., ROM / RAM, a magnetic disk, or an optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of this application. Example 3 According to an embodiment of this application, an intrusion detection device for implementing the above-mentioned intrusion detection method is also provided. As shown in FIG5 , the device includes an acquisition unit 501, a first detection unit 502, a second detection unit 503, and a third detection unit 504. An acquisition unit 501 is configured to acquire a target video frame to be detected, wherein the target video frame includes at least an image of a target area. A first detection unit 502 is configured to detect the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is configured to indicate whether image information of the target object exists in the target video frame. A second detection unit 503 is configured to detect the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object when the first detection result indicates that image information of the target object exists in the target video frame. The target key point detection module is configured to detect key points of the target object in the target video frame. A third detection unit 504 is configured to detect whether there is a regional intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, and obtain a second detection result.In the intrusion detection device provided in the third embodiment of the present application, an acquisition unit 501 acquires a target video frame to be detected, wherein the target video frame includes at least an image of a target area. A first detection unit 502 detects the target video frame using a target detection module in a target detection model to obtain a first detection result. The target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is used to indicate whether image information of the target object exists in the target video frame. A second detection unit 503 detects the target video frame using a target key point detection module in the target detection model to obtain coordinate information of key points of the target object, if the first detection result indicates that image information of the target object exists in the target video frame. The target key point detection module is configured to detect key points of the target object in the target video frame. A third detection unit 504 detects whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, and obtains a second detection result. This solves the technical problem in the related art of determining whether a person has intruded based on the positional relationship between the center point of a target object detection frame and the intrusion area, resulting in relatively low accuracy in determining whether an intrusion exists. In this solution, the target detection module in the target detection model first determines whether image information of the target object exists in the target video frame. If image information of the target object exists, the target key point detection module in the target detection model then detects key points of the target object in the target video frame to obtain coordinate information of the key points of the target object. Finally, based on the key point coordinate information and first coordinate information corresponding to the target area, detection of whether there is regional intrusion in the target video frame is performed. Compared with the prior art method of determining whether there is human intrusion based on the positional relationship between the center point of the target object detection frame and the intrusion area, detecting whether there is regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information can effectively avoid the problems of missed alarms and false alarms caused by factors such as the large range of the target object detection frame and the shooting angle, thereby achieving the technical effect of improving the accuracy of determining whether there is intrusion.Optionally, in the intrusion detection device provided in the third embodiment of the present application, the device further includes: a first frame selection unit configured to, before detecting the target video frame using the target detection module in the target detection model and obtaining the first detection result, select a target region in the target video frame using the rotation frame detection module in the target detection model to obtain multiple first initial frames and a first confidence level corresponding to a first initial frame in the multiple first initial frames, wherein the rotation frame detection module is used to detect the target region in the target video frame, and the first confidence level is used to indicate the accuracy of the first initial frame; a first determination unit configured to determine a first target frame from the multiple first initial frames based on the first confidence level; and a second determination unit configured to determine first coordinate information corresponding to the target region based on the first target frame. Optionally, in the intrusion detection device provided in the third embodiment of the present application, the third detection unit includes: a judgment module configured to determine a positional relationship between key point coordinate information and the first coordinate information; and a determination module configured to detect whether there is a regional intrusion in the target video frame based on the positional relationship and a preset detection strategy, thereby obtaining the second detection result. Optionally, in the intrusion detection device provided in Example 3 of the present application, the device further includes: a third determination unit, configured to detect whether there is regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area, and after obtaining the second detection result, determine the type of regional intrusion based on the positional relationship; a fourth determination unit, configured to determine the regional risk level based on the regional type corresponding to the target area; and a fifth determination unit, configured to determine a processing strategy based on the regional intrusion type and the regional risk level, wherein the regional intrusion event is processed according to the processing strategy. Optionally, in the intrusion detection device provided in Example 3 of the present application, the device further includes: a second frame selection unit, configured to, after detecting the target video frame through the target detection module in the target detection model and obtaining a first detection result, select the target area in the target video frame according to a preset time period through the rotating frame detection module in the target detection model when the first detection result indicates that there is no image information of the target object in the target video frame, to obtain multiple second initial frames and second confidences corresponding to the second initial frames in the multiple second initial frames; a sixth determination unit, configured to determine the second target frame from the multiple second initial frames based on the second confidence; and a seventh determination unit, configured to determine second coordinate information corresponding to the target area based on the second target frame.Optionally, in the intrusion detection device provided in Example 3 of the present application, the device further includes: a judgment unit, configured to, after determining the second coordinate information corresponding to the target area based on the second target frame, determine whether the second coordinate information is the same as the first coordinate information; and an updating unit, configured to update the second coordinate information to the first coordinate information if the second coordinate information is different from the first coordinate information. Optionally, in the intrusion detection device provided in Example 3 of the present application, the device further includes: an acquisition unit, configured to acquire training video frames and pre-process the training video frames to obtain processed training video frames before detecting the target video frames through the target key point detection module in the target detection model to obtain the key point coordinate information of the target object; a construction unit, configured to construct a training set based on the processed training video frames and the real labels corresponding to the processed training video frames to obtain a target training set, wherein the real labels are the real coordinate information of the detection box of the target object, or the real coordinate information of the target area, or the real coordinate information of the key points of the target object; and a training unit, configured to train the initial detection module, the initial rotation box detection module, and the initial key point detection module based on the target training set to obtain the target detection model. It should be noted that the acquisition unit 501, first detection unit 502, second detection unit 503, and third detection unit 504 described above correspond to steps S201 to S204 in Example 1. The examples and application scenarios implemented by these four units and the corresponding steps are the same, but are not limited to the content disclosed in Example 1. It should be noted that the above modules, as part of the apparatus, can be run in the computer terminal 10 provided in Example 1. It should be noted that the preferred implementation schemes involved in the above embodiments of this application are the same as those provided in Example 1, as well as the application scenarios and implementation processes, but are not limited to the solutions provided in Example 1. Example 4: The embodiments of this application may provide a computer terminal, which may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may be replaced with a terminal device such as a mobile terminal. Optionally, in this embodiment, the computer terminal may be located in at least one of multiple network devices in a computer network.In this embodiment, the computer terminal may execute program code for the following steps in the intrusion detection method: obtaining a target video frame to be detected, wherein the target video frame includes at least an image of a target area; detecting the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is configured to indicate whether image information of the target object exists in the target video frame; if the first detection result indicates that image information of the target object exists in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain coordinate information of key points of the target object, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; and detecting whether there is regional intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result. The computer terminal can execute program code for the following steps in the intrusion detection method: Before detecting the target video frame using the target detection module in the target detection model to obtain a first detection result, the method further includes: selecting a target area in the target video frame using the rotating frame detection module in the target detection model to obtain multiple first initial frames and a first confidence level corresponding to a first initial frame among the multiple first initial frames, wherein the rotating frame detection module is used to detect the target area in the target video frame, and the first confidence level is used to indicate the accuracy of the first initial frames; determining a first target frame from the multiple first initial frames based on the first confidence level; and determining first coordinate information corresponding to the target area based on the first target frame. The computer terminal can execute program code for the following steps in the intrusion detection method: detecting whether there is a regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area to obtain a second detection result includes: determining a positional relationship between the key point coordinate information and the first coordinate information; and detecting whether there is a regional intrusion in the target video frame based on the positional relationship and a preset detection strategy to obtain the second detection result. The computer terminal may execute program code for the following steps in the intrusion detection method: after detecting whether a regional intrusion exists in a target video frame based on key point coordinate information and first coordinate information corresponding to a target area, and obtaining a second detection result, the method further includes: determining a regional intrusion type based on a positional relationship; determining a regional risk level based on a regional type corresponding to the target area; and determining a processing strategy based on the regional intrusion type and the regional risk level, wherein the regional intrusion event is processed based on the processing strategy.The computer terminal can execute program code for the following steps in the intrusion detection method: After detecting a target video frame using a target detection module in a target detection model to obtain a first detection result, the method further includes: if the first detection result indicates that image information of the target object is absent in the target video frame, selecting a target area in the target video frame using a rotating frame detection module in the target detection model based on a preset time period to obtain multiple second initial frames and second confidence levels corresponding to second initial frames in the multiple second initial frames; determining a second target frame from the multiple second initial frames based on the second confidence level; and determining second coordinate information corresponding to the target area based on the second target frame. The computer terminal can execute program code for the following steps in the intrusion detection method: After determining second coordinate information corresponding to the target area based on the second target frame, the method further includes: determining whether the second coordinate information is identical to the first coordinate information; and if the second coordinate information is not identical to the first coordinate information, updating the second coordinate information to the first coordinate information. The computer terminal can execute program code for the following steps in the intrusion detection method: Before detecting the target video frame using the target key point detection module in the target detection model to obtain key point coordinate information of the target object, the method further includes: acquiring training video frames and preprocessing the training video frames to obtain processed training video frames; constructing a training set based on the processed training video frames and the true labels corresponding to the processed training video frames to obtain a target training set, where the true labels are the true coordinate information of the detection box of the target object, the true coordinate information of the target area, or the true coordinate information of the key points of the target object; and training the initial detection module, the initial rotation box detection module, and the initial key point detection module based on the target training set to obtain a target detection model. Optionally, FIG6 is a block diagram of the structure of a computer terminal according to an embodiment of the present application. As shown in FIG6 , the computer terminal 10 may include one or more (only one is shown in FIG6 ) processors 102 and a memory 104. oThe computing terminal 10 may also include a storage controller to control and manage the memory 104. The computing terminal 10 may also include a peripheral interface to connect to a radio frequency module, an audio module, a display screen, and the like. The memory may be used to store software programs and modules, such as the program instructions / modules corresponding to the intrusion detection method and apparatus described in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the aforementioned intrusion detection method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The processor can call information and applications stored in the memory through the transmission device to perform the following steps: obtaining a target video frame to be detected, wherein the target video frame includes at least an image of a target area; detecting the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is used to detect whether image information of the target object exists in the target video frame, and the first detection result is used to indicate whether image information of the target object exists in the target video frame; when the first detection result indicates that image information of the target object exists in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object, wherein the target key point detection module is used to detect key points of the target object in the target video frame; and detecting whether there is regional intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result. Optionally, the processor may further execute program code of the following steps: before detecting the target video frame using the target detection module in the target detection model to obtain a first detection result, the method may further include: selecting a target area in the target video frame using the rotating frame detection module in the target detection model to obtain multiple first initial frames and first confidences corresponding to the first initial frames in the multiple first initial frames, wherein the rotating frame detection module is used to detect the target area in the target video frame, and the first confidence is used to represent the accuracy of the first initial frame; determining a first target frame from the multiple first initial frames based on the first confidence; and determining first coordinate information corresponding to the target area based on the first target frame.Optionally, the processor may further execute program code for the following steps: detecting whether a regional intrusion occurs in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area, and obtaining the second detection result includes: determining a positional relationship between the key point coordinate information and the first coordinate information; and detecting whether a regional intrusion occurs in the target video frame based on the positional relationship and a preset detection strategy, to obtain the second detection result. Optionally, the processor may further execute program code for the following steps: after detecting whether a regional intrusion occurs in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area, and obtaining the second detection result, the method further includes: determining a regional intrusion type based on the positional relationship; determining a regional risk level based on the regional type corresponding to the target area; and determining a processing strategy based on the regional intrusion type and the regional risk level, wherein the regional intrusion event is processed based on the processing strategy. Optionally, the processor may further execute program code for the following steps: after detecting the target video frame using the target detection module in the target detection model to obtain a first detection result, the method may further include: if the first detection result indicates that image information of the target object is absent in the target video frame, selecting a target region in the target video frame using the rotating frame detection module in the target detection model based on a preset time period to obtain multiple second initial frames and second confidence levels corresponding to the second initial frames in the multiple second initial frames; determining a second target frame from the multiple second initial frames based on the second confidence levels; and determining second coordinate information corresponding to the target region based on the second target frame. Optionally, the processor may further execute program code for the following steps: after determining second coordinate information corresponding to the target region based on the second target frame, the method may further include: determining whether the second coordinate information is identical to the first coordinate information; and if the second coordinate information is different from the first coordinate information, updating the second coordinate information to the first coordinate information. Optionally, the processor may further execute program code of the following steps: before detecting the target video frame using the target key point detection module in the target detection model to obtain key point coordinate information of the target object, the method may further include: acquiring training video frames and preprocessing the training video frames to obtain processed training video frames; constructing a training set based on the processed training video frames and the true labels corresponding to the processed training video frames to obtain a target training set, where the true labels are the true coordinate information of the detection box of the target object, the true coordinate information of the target area, or the true coordinate information of the key points of the target object; and training the initial detection module, the initial rotation box detection module, and the initial key point detection module based on the target training set to obtain a target detection model.Those skilled in the art will appreciate that the structure shown in FIG6 is merely illustrative, and the computer terminal may also be a smartphone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile internet device (MID), a PAD, or other terminal device. FIG6 does not limit the structure of the aforementioned electronic devices. For example, the computer terminal 10 may include more or fewer components (such as a network interface, a display device, etc.) than those shown in FIG6 , or have a configuration different from that shown in FIG6 . Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by a program instructing the hardware associated with the terminal device. The program may be stored in a computer-readable storage medium, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Example 5 The embodiments of the present application also provide a computer-readable storage medium. Optionally, in this embodiment, the storage medium may be used to store program code executed by the intrusion detection method provided in the first embodiment. Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group. Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: obtaining a target video frame to be detected, wherein the target video frame includes at least an image of a target area; detecting the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is configured to indicate whether image information of the target object exists in the target video frame; if the first detection result indicates that image information of the target object exists in the target video frame, detecting the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; and detecting whether there is regional intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result.The storage medium is configured to store program code for executing the following steps: Before detecting the target video frame using the target detection module in the target detection model to obtain a first detection result, the method further includes: selecting a target region in the target video frame using the rotating frame detection module in the target detection model to obtain multiple first initial frames and a first confidence level corresponding to a first initial frame in the multiple first initial frames, wherein the rotating frame detection module is configured to detect the target region in the target video frame, and the first confidence level is configured to indicate the accuracy of the first initial frames; determining a first target frame from the multiple first initial frames based on the first confidence level; and determining first coordinate information corresponding to the target region based on the first target frame. The storage medium is configured to store program code for executing the following steps: detecting whether there is a region intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target region to obtain a second detection result includes: determining a positional relationship between the key point coordinate information and the first coordinate information; and detecting whether there is a region intrusion in the target video frame based on the positional relationship and a preset detection strategy to obtain the second detection result. The storage medium is configured to store program code for executing the following steps: after detecting whether a target video frame contains a regional intrusion based on key point coordinate information and first coordinate information corresponding to a target region, and obtaining a second detection result, the method further comprises: determining a regional intrusion type based on the positional relationship; determining a regional risk level based on the region type corresponding to the target region; and determining a processing strategy based on the regional intrusion type and the regional risk level, wherein the regional intrusion event is processed based on the processing strategy. The storage medium is configured to store program code for executing the following steps: after detecting the target video frame using an object detection module in an object detection model and obtaining a first detection result, the method further comprises: if the first detection result indicates that image information of a target object is absent in the target video frame, selecting a target region in the target video frame using a rotating frame detection module in the object detection model based on a preset time period, and obtaining multiple second initial frames and second confidence levels corresponding to the second initial frames in the multiple second initial frames; determining a second target frame from the multiple second initial frames based on the second confidence level; and determining second coordinate information corresponding to the target region based on the second target frame. The storage medium is configured to store program code for executing the following steps: after determining second coordinate information corresponding to the target area based on the second target frame, the method further includes: determining whether the second coordinate information is the same as the first coordinate information; and if the second coordinate information is not the same as the first coordinate information, updating the second coordinate information to the first coordinate information.The storage medium is configured to store program code for executing the following steps: Before detecting the target video frame using the target key point detection module in the target detection model to obtain key point coordinate information of the target object, the method further includes: acquiring training video frames and preprocessing the training video frames to obtain processed training video frames; constructing a training set based on the processed training video frames and the true labels corresponding to the processed training video frames to obtain a target training set, where the true labels are the true coordinate information of the detection box of the target object, the true coordinate information of the target region, or the true coordinate information of the key points of the target object; and training the initial detection module, the initial rotation box detection module, and the initial key point detection module based on the target training set to obtain a target detection model. The serial numbers of the above-mentioned embodiments of this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above-mentioned embodiments of this application, the description of each embodiment has its own emphasis. For portions not detailed in one embodiment, reference should be made to the relevant descriptions of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other divisions may be employed. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be through interfaces, or indirect couplings or communication connections between units or modules, and may be electrical or other forms. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of the present embodiments as needed. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. These integrated units may be implemented in either hardware or software functional units. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), a removable hard drive, a magnetic disk, or an optical disk. The above description is merely a preferred embodiment of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and such improvements and modifications should also be considered within the scope of protection of this application. Industrial Applicability The intrusion detection method provided in the embodiments of the present application first uses the target detection module in the target detection model to determine whether image information of the target object exists in the target video frame. If image information of the target object exists, the target key point detection module in the target detection model then detects key points of the target object in the target video frame to obtain key point coordinate information of the target object. Finally, based on the key point coordinate information and first coordinate information corresponding to the target area, it is detected whether there is a regional intrusion in the target video frame. Compared with the prior art method of determining whether a person has intruded based on the positional relationship between the center point of the target object detection frame and the intrusion area, detecting whether there is a regional intrusion in the target video frame based on the key point coordinate information and the first coordinate information can effectively avoid the problems of missed alarms and false alarms caused by factors such as the large range of the target object detection frame and the shooting angle, thereby achieving the technical effect of improving the accuracy of determining whether there is an intrusion.

Claims

Claims 1. An intrusion detection method, comprising: A target video frame to be detected is obtained, wherein the target video frame includes at least an image of a target area; the target video frame is detected by a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is used to detect whether image information of the target object exists in the target video frame, and the first detection result is used to characterize whether image information of the target object exists in the target video frame; when the first detection result characterizes the presence of image information of the target object in the target video frame, the target video frame is detected by a target key point detection module in the target detection model to obtain key point coordinate information of the target object, wherein the target key point detection module is used to detect key points of the target object in the target video frame; and whether there is regional intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result.

2. The method according to claim 1, wherein: Before detecting the target video frame using the target detection module in the target detection model to obtain a first detection result, the method further includes: selecting a target area in the target video frame using a rotating frame detection module in the target detection model to obtain multiple first initial frames and a first confidence level corresponding to a first initial frame among the multiple first initial frames, wherein the rotating frame detection module is used to detect the target area in the target video frame, and the first confidence level is used to represent the accuracy of the first initial frame; determining a first target frame from the multiple first initial frames based on the first confidence level; and determining first coordinate information corresponding to the target area based on the first target frame.

3. The method according to claim 2, wherein: Detecting whether there is area intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area, and obtaining the second detection result includes: determining the positional relationship between the key point coordinate information and the first coordinate information; detecting whether there is area intrusion in the target video frame based on the positional relationship and a preset detection strategy, and obtaining the second detection result. 24 4. The method according to claim 3, wherein: After detecting whether there is an area intrusion in the target video frame based on the key point coordinate information and the first coordinate information corresponding to the target area and obtaining a second detection result, the method further includes: determining an area intrusion type based on the positional relationship; determining an area risk level based on the area type corresponding to the target area; and determining a processing strategy based on the area intrusion type and the area risk level, wherein the area intrusion event is processed based on the processing strategy.

5. The method according to claim 1, wherein: After detecting the target video frame using the target detection module in the target detection model to obtain a first detection result, the method further includes: when the first detection result indicates that image information of the target object is absent in the target video frame, selecting a target area in the target video frame based on a preset time period using the rotating frame detection module in the target detection model to obtain a plurality of second initial frames and second confidences corresponding to second initial frames in the plurality of second initial frames; determining a second target frame from the plurality of second initial frames based on the second confidences; and determining second coordinate information corresponding to the target area based on the second target frame.

6. The method according to claim 5, wherein: After determining the second coordinate information corresponding to the target area based on the second target frame, the method further includes: determining whether the second coordinate information is the same as the first coordinate information; if the second coordinate information is not the same as the first coordinate information, updating the second coordinate information to the first coordinate information.

7. The method according to claim 1, wherein: Before detecting the target video frame using the target key point detection module in the target detection model to obtain key point coordinate information of the target object, the method further includes: acquiring training video frames and preprocessing the training video frames to obtain processed training video frames; constructing a training set based on the processed training video frames and real labels corresponding to the processed training video frames to obtain a target training set, wherein the real labels are real coordinate information of a detection box of the target object, real coordinate information of a target area, or real coordinate information of a key point of the target object; The initial detection module, the initial rotation box detection module, and the initial key point detection module are trained according to the target training set to obtain the target detection model.

8. An intrusion detection method, comprising: Obtain a target video frame to be detected uploaded by a client, wherein the target video frame includes at least an image of a target area; detect the target video frame in a cloud server using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is used to detect whether image information of a target object exists in the target video frame, and the first detection result is used to indicate whether image information of the target object exists in the target video frame; when the first detection result indicates that image information of the target object exists in the target video frame, detect the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object, wherein the target key point detection module is used to detect key points of the target object in the target video frame; detect whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, to obtain a second detection result; and return the second detection result to the client.

9. An intrusion detection device, comprising: An acquisition unit is configured to acquire a target video frame to be detected, wherein the target video frame includes at least an image of a target area; a first detection unit is configured to detect the target video frame using a target detection module in a target detection model to obtain a first detection result, wherein the target detection module is configured to detect whether image information of a target object exists in the target video frame, and the first detection result is used to characterize whether image information of the target object exists in the target video frame; a second detection unit is configured to detect the target video frame using a target key point detection module in the target detection model to obtain key point coordinate information of the target object when the first detection result characterizes the presence of image information of the target object in the target video frame, wherein the target key point detection module is configured to detect key points of the target object in the target video frame; and a third detection unit is configured to detect whether there is an area intrusion in the target video frame based on the key point coordinate information and first coordinate information corresponding to the target area, and obtain a second detection result.

10. A computer-readable storage medium comprising a stored program, wherein: When the program is running, the device where the storage medium is located is controlled to execute any one of claims 1 to 8 Intrusion detection methods.

11. An electronic device, comprising: a memory storing an executable program; A processor is configured to run the program, wherein the program executes the intrusion detection method according to any one of claims 1 to 8 when running. 27

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