Emergency parking belt occupation early warning method based on key point posture detection
By combining surveillance cameras and ground loop coils, a key point attitude detection module was constructed, which enabled accurate early warning of emergency parking lane occupancy. This solved the problems of low accuracy and efficiency in existing technologies and reduced safety hazards on highways.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INTELLIGENT INTER CONNECTION TECH CO LTD
- Filing Date
- 2023-10-08
- Publication Date
- 2026-06-12
AI Technical Summary
The accuracy and efficiency of emergency stopping lane occupancy warning technologies are low in the current technology, resulting in high safety risks on highways.
By acquiring dynamic video of the target monitoring area through surveillance cameras, a key point attitude detection module is constructed to extract information about vehicles entering the emergency stopping lane, generate early warning information, and cancel the early warning when the vehicle leaves using inductive loop sensors.
This improved the accuracy and efficiency of emergency stopping lane occupancy warnings, reducing safety hazards on highways.
Smart Images
Figure CN117409600B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of detection and early warning technology, specifically to an emergency parking lane occupancy early warning method based on key point attitude detection. Background Technology
[0002] Emergency stopping lanes are temporary stopping areas on highways and first-class roads for vehicles to stop temporarily due to breakdowns or other reasons. Emergency stopping lanes are for emergency use only and must not be occupied without justifiable cause. However, many vehicles still drive into emergency stopping lanes. To prevent safety hazards on highways, a solution is needed.
[0003] In summary, existing technologies suffer from low accuracy and efficiency in warning of emergency stopping lane occupancy, leading to higher safety risks on highways. Summary of the Invention
[0004] This disclosure provides an emergency stopping lane occupancy warning method based on key point attitude detection, which solves the technical problem that the low accuracy and efficiency of emergency stopping lane occupancy warning in the prior art leads to high safety hazards on highways.
[0005] According to a first aspect of this disclosure, an emergency stopping lane occupancy warning method based on key point attitude detection is provided, comprising: acquiring dynamic video of a target monitoring area through the monitoring camera, wherein the target monitoring area includes a target emergency stopping lane and a first lane adjacent to the target emergency stopping lane; constructing a key point attitude detection module to extract the dynamic video, and determining, based on the key point attitude detection module, that a vehicle has entered the target emergency stopping lane; acquiring the license plate information of the vehicle and generating a first warning message; generating a sign lighting command and an alarm command based on the first warning message; and deactivating the first warning message when the ground induction coil senses that the vehicle has left the target emergency stopping lane and entered the first lane.
[0006] According to a second aspect of this disclosure, an emergency stopping lane occupancy warning system based on key point attitude detection is provided, comprising: a dynamic video acquisition module, wherein the dynamic video acquisition module is used to acquire dynamic video of a target monitoring area through the monitoring camera, wherein the target monitoring area includes a target emergency stopping lane and a first lane adjacent to the target emergency stopping lane; a key point attitude detection module construction module, wherein the key point attitude detection module construction module is used to construct a key point attitude detection module, extract the dynamic video, and determine that a vehicle has entered the target emergency stopping lane based on the key point attitude detection module; a first warning information acquisition module, wherein the first warning information acquisition module is used to acquire the license plate information of the vehicle and generate first warning information; a sign lighting instruction acquisition module, wherein the sign lighting instruction acquisition module is used to generate a sign lighting instruction and an alarm instruction based on the first warning information; and a first warning information cancellation module, wherein the first warning information cancellation module is used to cancel the first warning information when the ground induction coil senses that the vehicle has left the target emergency stopping lane and entered the first lane.
[0007] According to a third aspect of this disclosure, a computer device includes a memory and a processor, the memory storing a computer program and the processor implementing the method described in any one of the first aspects.
[0008] According to a fourth aspect of this disclosure, a computer-readable storage medium has a computer program stored thereon, which, when executed by a processor, implements the method described in any one of the first aspects.
[0009] One or more technical solutions provided in this disclosure have at least the following technical effects or advantages: According to the method adopted in this disclosure, dynamic video of a target monitoring area is acquired through the monitoring camera, wherein the target monitoring area includes a target emergency stopping lane and a first lane adjacent to the target emergency stopping lane; a key point attitude detection module is constructed to extract the dynamic video, and the vehicle's entry into the target emergency stopping lane is determined based on the key point attitude detection module; the vehicle's license plate information is acquired, and a first warning message is generated; a sign lighting command and an alarm command are generated based on the first warning message; when the ground induction coil senses that the vehicle has left the target emergency stopping lane and entered the first lane, the first warning message is deactivated. This solves the technical problem in the prior art where the low accuracy and efficiency of emergency stopping lane occupancy warnings lead to high highway safety hazards, achieving the goal of improving the accuracy and efficiency of emergency stopping lane occupancy warnings, and thus reducing highway safety hazards.
[0010] It should be understood that the description in this section is not intended to highlight key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0012] Figure 1 A flowchart illustrating the emergency parking lane occupancy warning method based on key point attitude detection provided in this embodiment of the disclosure;
[0013] Figure 2 This is a logical diagram illustrating the connection relationship of multiple devices in the emergency parking lane occupancy warning method based on key point attitude detection according to an embodiment of this disclosure;
[0014] Figure 3 This is a schematic diagram of the structure of an emergency parking lane occupancy warning system based on key point attitude detection provided in an embodiment of the present disclosure;
[0015] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure.
[0016] Explanation of reference numerals in the attached figures: Dynamic video acquisition module 11, key point posture detection module construction module 12, first warning information acquisition module 13, sign light command acquisition module 14, first warning information cancellation module 15, computer device 100, processor 101, memory 102, bus 103. Detailed Implementation
[0017] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.
[0018] Example 1
[0019] The emergency parking lane occupancy warning method based on key point attitude detection provided in this disclosure is referred to below. Figure 1 and Figure 2 As explained, the method includes:
[0020] The method provided in this disclosure includes:
[0021] The surveillance camera acquires dynamic video of the target monitoring area, wherein the target monitoring area includes the target emergency stopping lane and the first lane adjacent to the target emergency stopping lane;
[0022] Specifically, surveillance cameras are connected to monitor the road. Furthermore, the surveillance cameras monitor the target monitoring area, capturing dynamic video of the target monitoring area. Further, the target monitoring area includes the target emergency stopping lane for which a parking warning is to be issued, and the lane adjacent to the target emergency stopping lane, designated as the first lane.
[0023] A key point attitude detection module is constructed to extract the dynamic video, and the vehicle is determined to have entered the target emergency stopping lane based on the key point attitude detection module.
[0024] Specifically, a key point pose module is constructed by acquiring vehicle 3D model data. Further, dynamic video is extracted and split into multiple images, which are then input into the key point pose detection module. This module identifies the vehicle's driving direction, thereby determining whether the vehicle has entered the target emergency stopping lane.
[0025] Obtain the license plate information of the vehicle and generate a first warning message;
[0026] Generate a sign light-up command and an alarm command based on the first warning information;
[0027] When the ground induction coil detects that the vehicle has left the target emergency stopping lane and entered the first lane, the first warning information is deactivated.
[0028] Specifically, when a vehicle enters the target emergency stopping lane, the vehicle's license plate information is obtained through a surveillance camera, and a first warning message is generated.
[0029] Furthermore, a sign can be installed several tens of meters behind the target emergency stopping lane. A sign illumination command is generated based on the first warning information, and the sign is then illuminated according to the command. Further, an alarm command is generated based on the sign illumination command, which then connects to the alarm system and triggers an alarm.
[0030] Furthermore, the target emergency stopping lane is connected to an inductive loop detector. The inductive loop detector senses the departure status of vehicles. Specifically, when the inductive loop detector detects a vehicle leaving the target emergency stopping lane and entering the first lane, the first warning information is deactivated.
[0031] This embodiment addresses the technical problem in the prior art where the low accuracy and efficiency of emergency lane occupancy warnings lead to high safety hazards on highways. It aims to improve the accuracy and efficiency of emergency lane occupancy warnings, thereby reducing highway safety hazards.
[0032] The method provided in this disclosure also includes:
[0033] Based on big data, acquire vehicle data and build a target vehicle database;
[0034] The vehicle 3D model is extracted from the target vehicle database to train the key point pose detection module, and the vehicle result is obtained through the key point pose detection module.
[0035] Data on variant vehicle models is acquired, the key point attitude detection module is trained, and the variant vehicle results are obtained through the key point attitude detection module.
[0036] Specifically, based on big data, vehicle data is used as an index for retrieval to obtain multiple vehicle data sets. A target vehicle database is then constructed based on this data, which is used to train a keypoint pose detection module to identify vehicles. This vehicle data includes 3D model data of the vehicles.
[0037] Further, 3D vehicle models from the target vehicle database are extracted to train the keypoint pose detection module. Training continues until the keypoint pose detection module converges, resulting in a trained module. This module is then validated using vehicle data to obtain validation results. Training is complete when the validation result exceeds a preset validation threshold. If the validation result is less than or equal to the preset threshold, training continues using the target vehicle database until the validation result exceeds the preset threshold. The preset validation threshold is a custom-defined accuracy threshold for the module; for example, a preset validation threshold of 95% might be obtained.
[0038] Furthermore, given the diverse forms of vehicles, data on various vehicle variants is acquired based on big data. For example, a sedan can be modified into an off-road vehicle. This variant data is then extracted and used as input to a keypoint pose detection module. The module trains the variant data, enabling it to identify and effectively detect these vehicles.
[0039] Training the keypoint pose module can improve its recognition accuracy, thereby increasing detection efficiency.
[0040] The method provided in this disclosure also includes:
[0041] Multiple images are obtained by splitting the dynamic video, and the vehicle is obtained by detecting and recognizing the multiple images.
[0042] The system iterates through the multiple images to extract images including the vehicle, estimates the vehicle's posture using the key point pose detection module, and determines that the vehicle has entered the target emergency stopping lane.
[0043] Specifically, keyframes are extracted from the dynamic video in chronological order, and each keyframe is treated as an image. The content data in the image is then identified. If data is obtained through identification, the image is extracted. Since emergency stopping lanes are mostly located on highways, and there are no pedestrians on highways, the data obtained through image recognition is for vehicles.
[0044] Furthermore, each image is identified sequentially, and images containing vehicles are extracted. The key point pose detection module determines the vehicle's direction of travel by analyzing the vehicle's basic structure, the angle between the wheels and the ground, and whether the wheels and body are aligned.
[0045] Among these features, by determining the vehicle's direction of travel, it is possible to quickly detect and issue warnings when a vehicle enters an emergency stopping lane.
[0046] The method provided in this disclosure also includes:
[0047] The outline of the vehicle is annotated to obtain the vehicle outline annotation result;
[0048] Based on the vehicle width, vehicle height, and overall length, the basic vehicle structure, including the engine compartment, driver's cab, and luggage compartment, is obtained.
[0049] The vehicle's direction of travel is obtained based on the basic structure described above;
[0050] Based on the vehicle outline annotation results and the vehicle's driving direction, the vehicle's entry direction is obtained, and it is determined that the vehicle has entered the target emergency stopping lane.
[0051] Specifically, the vehicle's outline in the image is annotated to obtain the vehicle outline annotation results. This includes annotating the vehicle's highest, lowest, leftmost, and rightmost points. Further, proportional data of the vehicle's width, height, and overall length are obtained to make a preliminary judgment on the vehicle's 3D state, obtaining the basic structure of the engine compartment, driver's cab, and trunk, and a preliminary determination of the vehicle's direction of travel. However, due to the different types of vehicles, it is not possible to obtain the basic structure of the engine compartment, driver's cab, and trunk—i.e., the vehicle's orientation—for all vehicles using proportional data of width, height, and overall length. Therefore, the preliminary results still need to be verified.
[0052] Furthermore, based on the vehicle outline marking results and the vehicle's driving direction, the initial direction of the vehicle's entry is obtained, thereby determining whether the vehicle's entry direction is the direction of the target emergency stopping lane.
[0053] By making a preliminary judgment on the direction of vehicle entry, the accuracy of detecting the occupancy of the target emergency stopping lane can be improved.
[0054] The method provided in this disclosure also includes:
[0055] The vehicle is annotated in detail to obtain the front and rear wheel track widths.
[0056] Based on the wheel track, the changes in the approach angle, departure angle, and longitudinal passing angle of the vehicle when it enters the slope are obtained, and the gradeability is determined.
[0057] The direction in which the vehicle enters is determined based on the distribution of the wheels and the vehicle body.
[0058] Specifically, the vehicle is annotated in detail to obtain the distance between the front and rear wheels. Further, based on the wheel track, the changes in the approach angle, departure angle, and longitudinal clearance angle when the vehicle enters the slope are obtained to determine the gradeability. The approach angle is the maximum angle between the horizontal plane and the plane tangent to the outer edge of the front tire. The departure angle is the angle between the ground and the tangent drawn from the rearmost point of the fully loaded vehicle to the rear wheel when the vehicle is stationary. The longitudinal clearance angle is the minimum acute angle formed by tangents drawn from the outer edges of the front and rear wheels respectively to the lower part of the vehicle body in the side view when the vehicle is fully loaded or stationary. The gradeability is expressed as a percentage of the ratio of the height difference between the start and end points of the slope to its horizontal distance. Furthermore, the emergency stopping lane and the adjacent lane can be at different elevations; therefore, when a vehicle enters the emergency stopping lane from the lane adjacent to the emergency stopping lane, the wheels will cross planes at different elevations, and the angle between the wheels and the ground will change.
[0059] Furthermore, the vehicle's direction of travel is determined based on the distribution of the wheels and the vehicle body. Specifically, when the wheel direction is the same as the vehicle body direction, the vehicle travels in a straight line. When the wheel direction is not the same as the vehicle body direction, the vehicle travels in a turning direction.
[0060] Among these methods, obtaining the vehicle's direction of travel based on the angle between the wheel and the ground and the wheel's direction can improve the accuracy of obtaining emergency stopping lane detection warnings.
[0061] The method provided in this disclosure also includes:
[0062] Determine the vehicle's position in the image to obtain the vehicle recognition result;
[0063] The vehicle recognition result is denoised using a denoising formula to obtain the image result;
[0064] Based on the image results, it is verified that the vehicle is in the target emergency stopping lane, and it is determined that the vehicle has entered the target emergency stopping lane.
[0065] Specifically, after determining the vehicle's direction of entry, the vehicle's location in the image is identified to obtain an identification result, which is used to determine whether the vehicle is within the target emergency stopping lane. Further, the vehicle identification result is denoised using a denoising formula to obtain the final image. Specifically, each pixel in the image is denoised sequentially using the denoising formula. Accordingly, the denoising order can be from top to bottom and from left to right. Finally, the image result is used to verify that the vehicle is within the target emergency stopping lane, confirming that the vehicle has entered the target emergency stopping lane.
[0066] Verifying that a vehicle has entered the target emergency stopping lane can improve the accuracy of determining whether the emergency stopping lane is occupied.
[0067] The method provided in this disclosure also includes:
[0068] The denoising formula is:
[0069]
[0070] Wherein, the denoised image is u, w is the similarity weight between pixels x and y, I is the total number of pixels, and v is the distance between pixels x and y.
[0071] Specifically, the pixels are input into a denoising formula, which is used to denoise the image. Further, the algorithm calculates the estimated value of each pixel as a weighted average of all pixels in the image, but the weights depend on the similarity between pixels x and y. That is, it examines image patches consisting of a few pixels in an image, identifies other similar image patches in the entire image, and performs a weighted average on them.
[0072] Denoising an image using a denoising formula can improve the clarity of the image and allow for accurate judgment of the image results.
[0073] Example 2
[0074] Based on the same inventive concept as the emergency parking lane occupancy warning method based on key point attitude detection in the foregoing embodiments, the following is referred to Figure 3 As an explanation, this disclosure also provides an emergency parking lane occupancy warning system based on key point attitude detection, the system comprising:
[0075] The dynamic video acquisition module 11 is used to acquire dynamic video of a target monitoring area through the surveillance camera, wherein the target monitoring area includes a target emergency stopping lane and a first lane adjacent to the target emergency stopping lane.
[0076] Key point attitude detection module construction module 12 is used to construct a key point attitude detection module, extract the dynamic video, and determine the vehicle entering the target emergency stopping lane based on the key point attitude detection module.
[0077] The first warning information acquisition module 13 is used to acquire the license plate information of the vehicle and generate the first warning information.
[0078] The sign lighting instruction acquisition module 14 is used to generate a sign lighting instruction and an alarm instruction based on the first warning information.
[0079] The first warning information cancellation module 15 is used to cancel the first warning information when the ground induction coil senses that the vehicle has left the target emergency stopping lane and entered the first lane.
[0080] Furthermore, the system also includes:
[0081] A target vehicle database construction module is used to acquire vehicle data based on big data and construct a target vehicle database.
[0082] The vehicle result acquisition module is used to extract the vehicle 3D model from the target vehicle database to train the key point pose detection module, and obtain the vehicle result through the key point pose detection module.
[0083] A variant vehicle result acquisition module is used to acquire variant vehicle model data, train the key point attitude detection module, and obtain variant vehicle results through the key point attitude detection module.
[0084] Furthermore, the system also includes:
[0085] An image detection module is used to obtain multiple images based on the dynamic video segmentation, detect and identify the multiple images, and obtain the vehicle.
[0086] The image traversal module is used to traverse the multiple images to extract images including the vehicle, estimate the vehicle posture through the key point posture detection module, and determine that the vehicle has entered the target emergency stopping lane.
[0087] Furthermore, the system also includes:
[0088] A vehicle outline annotation result acquisition module is used to annotate the outline of the vehicle and obtain the vehicle outline annotation result.
[0089] A vehicle basic structure acquisition module is used to obtain the basic vehicle structure of the engine compartment, driver's cab, and luggage compartment based on the vehicle width, vehicle height, and overall vehicle length.
[0090] A vehicle driving direction obtaining module, wherein the vehicle driving direction obtaining module is used to obtain the vehicle driving direction according to the basic structure;
[0091] The vehicle driving direction determination module is used to obtain the vehicle entry direction based on the vehicle outline annotation result and the vehicle driving direction, and to determine that the vehicle has entered the target emergency stopping lane.
[0092] Furthermore, the system also includes:
[0093] Wheel track acquisition module, which is used to perform detailed annotation on the vehicle and obtain the front wheel track and rear wheel track;
[0094] A gradeability acquisition module is used to obtain the changes in the approach angle, departure angle, and longitudinal passing angle of the vehicle when it enters the road, based on the wheel track, and to determine the gradeability.
[0095] A distribution direction acquisition module is used to determine the vehicle's driving direction based on the distribution direction of the wheels and the vehicle body.
[0096] Furthermore, the system also includes:
[0097] A vehicle recognition result acquisition module is used to determine the position of the vehicle in the image and obtain the vehicle recognition result;
[0098] An image result acquisition module is used to denoise the vehicle recognition result using a denoising formula to obtain an image result;
[0099] An image result determination module is used to verify, based on the image result, that the vehicle is in the target emergency stopping lane and to determine that the vehicle has entered the target emergency stopping lane.
[0100] Furthermore, the system also includes:
[0101] The denoising formula acquisition module is used to obtain the denoising formula as follows:
[0102]
[0103] The denoising formula processing module is used in which the denoised image is u, w is the similarity weight between pixels x and y, I is the total number of pixels, and v is the distance between pixels x and y.
[0104] The specific example of the emergency parking lane occupancy warning method based on key point attitude detection in Embodiment 1 described above is also applicable to the emergency parking lane occupancy warning system based on key point attitude detection in this embodiment. Through the foregoing detailed description of the emergency parking lane occupancy warning method based on key point attitude detection, those skilled in the art can clearly understand the emergency parking lane occupancy warning system based on key point attitude detection in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.
[0105] Example 3
[0106] Figure 4 This is a schematic diagram based on the third embodiment of the present disclosure, as shown below. Figure 4 As shown, the computer device 100 in this disclosure may include a processor 101 and a memory 102.
[0107] Memory 102 is used to store programs. Memory 102 may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDRSDRAM), etc.; memory may also include non-volatile memory, such as flash memory. Memory 102 is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc. The computer programs, computer instructions, etc., can be partitioned and stored in one or more memories 102. Furthermore, the computer programs, computer instructions, data, etc., can be accessed by processor 101.
[0108] The aforementioned computer programs and instructions can be stored in one or more partitions of memory 102. Furthermore, the aforementioned computer programs and instructions can be invoked by processor 101.
[0109] The processor 101 is configured to execute the computer program stored in the memory 102 to implement the various steps in the methods described in the above embodiments.
[0110] For details, please refer to the relevant descriptions in the preceding method embodiments.
[0111] The processor 101 and the memory 102 can be independent structures or integrated structures. When the processor 101 and the memory 102 are independent structures, the memory 102 and the processor 101 can be coupled together via the bus 103.
[0112] The computer device in this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same, and will not be repeated here.
[0113] According to embodiments of this disclosure, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed, implements the steps provided in any of the above embodiments.
[0114] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.
[0115] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. An emergency parking lane occupancy warning method based on key point attitude detection, characterized in that, The method is applied to an emergency parking lane occupancy warning system based on key point attitude detection. The system is communicatively connected to a monitoring camera and a ground loop coil. The method includes: The surveillance camera acquires dynamic video of the target monitoring area, wherein the target monitoring area includes the target emergency stopping lane and the first lane adjacent to the target emergency stopping lane; A key point attitude detection module is constructed to extract the dynamic video, and the vehicle is determined to have entered the target emergency stopping lane based on the key point attitude detection module. Obtain the license plate information of the vehicle and generate a first warning message; Generate a sign light-up command and an alarm command based on the first warning information; When the ground induction coil detects that the vehicle has left the target emergency stopping lane and entered the first lane, the first warning information is deactivated. The method for extracting the dynamic video and determining whether a vehicle has entered the target emergency stopping lane based on the key point attitude detection module includes: Multiple images are obtained by splitting the dynamic video, and the vehicle is obtained by detecting and recognizing the multiple images. The images including the vehicle are extracted by traversing the multiple images, and the vehicle posture is estimated by the key point posture detection module to determine whether the vehicle has entered the target emergency stopping lane. The method of traversing the multiple images to extract images including the vehicle, estimating the vehicle's posture using the key point pose detection module, and determining that the vehicle has entered the target emergency stopping lane includes: The outline of the vehicle is annotated to obtain the vehicle outline annotation result; Based on the vehicle width, vehicle height, and overall length, the basic vehicle structure, including the engine compartment, driver's cab, and luggage compartment, is obtained. The vehicle's direction of travel is obtained based on the basic structure described above; Based on the vehicle outline annotation results and the vehicle's driving direction, the vehicle's entry direction is obtained, and it is determined that the vehicle has entered the target emergency stopping lane.
2. The method as described in claim 1, characterized in that, The method for constructing the key point pose detection module includes: Based on big data, acquire vehicle data and build a target vehicle database; The vehicle 3D model is extracted from the target vehicle database to train the key point pose detection module, and the vehicle result is obtained through the key point pose detection module. Data on variant vehicle models is acquired, the key point attitude detection module is trained, and the variant vehicle results are obtained through the key point attitude detection module.
3. The method as described in claim 1, characterized in that, The method of traversing the multiple images to extract images including the vehicle, estimating the vehicle posture through the key point pose detection module, and determining that the vehicle has entered the target emergency stopping lane, further includes: The vehicle is annotated in detail to obtain the front and rear wheel track widths. Based on the wheel track, the changes in the approach angle, departure angle, and longitudinal passing angle of the vehicle when it enters the slope are obtained, and the gradeability is determined. The direction in which the vehicle enters is determined based on the distribution of the wheels and the vehicle body.
4. The method as described in claim 1, characterized in that, The method of traversing the multiple images to extract images including the vehicle, estimating the vehicle posture through the key point pose detection module, and determining that the vehicle has entered the target emergency stopping lane, further includes: Determine the vehicle's position in the image to obtain the vehicle recognition result; The vehicle recognition result is denoised using a denoising formula to obtain the image result; Based on the image results, it is verified that the vehicle is in the target emergency stopping lane, and it is determined that the vehicle has entered the target emergency stopping lane.
5. The method as described in claim 4, characterized in that, The method includes: The denoising formula is: Wherein, the denoised image is u, w is the similarity weight between pixels x and y, I is the total number of pixels, and v is the distance between pixels x and y.
6. An emergency parking lane occupancy warning system based on key point attitude detection, characterized in that, For implementing the emergency parking lane occupancy warning method based on key point attitude detection as described in any one of claims 1-5, the system comprises: A dynamic video acquisition module is used to acquire dynamic video of a target monitoring area through the surveillance camera, wherein the target monitoring area includes a target emergency stopping lane and a first lane adjacent to the target emergency stopping lane; A key point attitude detection module construction module is used to construct a key point attitude detection module, extract the dynamic video, and determine whether the vehicle has entered the target emergency stopping lane based on the key point attitude detection module. The first warning information acquisition module is used to acquire the license plate information of the vehicle and generate the first warning information. A sign lighting instruction acquisition module is used to generate a sign lighting instruction and an alarm instruction based on the first warning information; The first warning information cancellation module is used to cancel the first warning information when the ground induction coil senses that the vehicle has left the target emergency stopping lane and entered the first lane.
7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-5.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.