A multi-mode authorization management method and device based on machine vision and a medium
By setting up independent cameras in different areas and combining them with machine vision algorithms, vehicles without the right to pass can be identified and asked to leave in advance during peak hours, which solves the problem of low traffic efficiency during peak hours and achieves efficient and accurate vehicle management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSPUR GENERSOFT CO LTD
- Filing Date
- 2024-07-15
- Publication Date
- 2026-07-24
AI Technical Summary
In areas such as large manufacturing enterprises, company offices, and large stadiums, when traffic flow is concentrated during peak hours, conventional access control systems result in low traffic efficiency, and manual identification of vehicle access rights is prone to misjudgment and omission.
A multi-mode authorization and control method based on machine vision is adopted. Independent cameras are set up in the entrance area and the intended entry area. Different control modes are used at different times. During off-peak hours, the entrance camera recognizes the license plate and determines whether to allow or deny passage. During peak hours, the camera in the intended entry area identifies and persuades vehicles without permission to leave in advance.
This improves traffic efficiency, prevents vehicles from turning back after reaching the entrance, reduces misjudgments, avoids the negative impact of over-control, and ensures the effectiveness and efficiency of vehicle traffic management.
Smart Images
Figure CN118887650B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of machine vision, specifically to a multi-mode authorization control method, device, and medium based on machine vision. Background Technology
[0002] Generally speaking, in areas such as shopping malls and supermarkets, where traffic flow is relatively dispersed and peak hours are not concentrated, conventional machine vision-based license plate recognition systems, supplemented by access control systems, can be used to manage vehicle access permits.
[0003] For large manufacturing enterprises, company offices, large stadiums, and other areas, there is a concentrated flow of vehicles entering and exiting during peak hours such as commuting and the end of events. If conventional access control opening and closing methods are continued during peak hours, it will greatly reduce traffic efficiency and cause large-scale congestion.
[0004] At this time, the vehicle access permit management department may adopt a method of "always open access control" to allow all vehicles to pass, or "always open access control + manual identification of vehicle access rights" to authorize passage, thereby increasing traffic efficiency.
[0005] While keeping access control open all the time can improve traffic efficiency, it also negates the original purpose of vehicle access control. On the other hand, using a combination of keeping access control open all the time and manually identifying vehicle access rights means that human judgment and experience are indispensable, which can lead to misjudgments and omissions, and potentially result in unauthorized vehicles passing through the access control system. Summary of the Invention
[0006] To address the aforementioned issues, this application proposes a multi-mode authorization control method based on machine vision, applied in an authorization control system. The authorization control system includes at least: an access control device installed in the entrance area, a first camera installed at the access control device, and a second camera installed in the intended entry area. The method includes: Based on the current time, determine the current authorization and control mode; If the authorization control mode is the off-peak authorization control mode, the license plate of the designated vehicle to be identified is recognized based on the first camera, and the access control device is controlled to release the designated vehicle according to the recognition result and the access permit rules. If the authorization control mode is the peak period authorization control mode, the license plate of the designated vehicle to be identified is recognized based on the second camera, and the vehicle is guided and persuaded to leave according to the recognition result and the passage permit rules.
[0007] On the other hand, this application also proposes a multi-mode authorization control device based on machine vision, including: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform, for example, the machine vision-based multi-mode authorization control method described above.
[0008] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, characterized in that the computer-executable instructions are configured as: the multi-mode authorization control method based on machine vision described in the above example.
[0009] The multi-modal authorization control method based on machine vision proposed in this application can bring the following beneficial effects: Compared to traditional vehicle access authorization and control methods, this approach involves installing separate cameras in the entrance and intended entry areas. These cameras identify vehicles and execute different actions depending on the time and control mode. During peak hours, vehicles can be identified in advance in the intended entry area, allowing unauthorized vehicles to be asked to leave. This prevents vehicles from returning after reaching the entrance and also allows temporarily parked vehicles to be moved, increasing traffic efficiency without reducing the intensity of vehicle control. During off-peak hours, since traffic efficiency is generally high, advance departure is not required to avoid the negative impact of over-control on businesses. Attached Figure Description
[0010] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating the multi-mode authorization control method based on machine vision in the embodiments of this application; Figure 2 This is a flowchart illustrating an off-peak authorization control mode under one scenario in an embodiment of this application. Figure 3 This is a flowchart illustrating a peak-hour authorization control mode under one scenario in an embodiment of this application. Figure 4 This is a schematic diagram of a machine vision-based multi-mode authorization and control device in an embodiment of this application. Detailed Implementation
[0011] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0012] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.
[0013] This application provides a machine vision-based multi-mode authorization control method, applied in an authorization control system. The authorization control system includes at least: an access control device installed in the entrance area, a first camera installed at the access control device, and a second camera installed in the intended entry area.
[0014] Authorization control systems can be applied in large manufacturing enterprises, company offices, large stadiums, and other similar areas, which are referred to as controlled areas. Access control devices are typically installed at the entrance of these controlled areas. Intended entry areas refer to the areas outside the entrance area where vehicles entering these areas have a high probability of continuing towards the access control devices to enter the controlled area. For example, multiple statistical points can be set up in the area outside the entrance area to track the routes of vehicles passing through each point. When more than a certain percentage (e.g., 80%) of vehicles at a given statistical point enter the driving path towards the access control devices, that point is considered to be within the intended entry area. Connecting all the statistical points that meet this rule, and then filtering out those points that are outside the second preset distance, reveals the intended entry areas. Alternatively, intended entry areas can be manually defined based on actual routes, as long as these areas are farther from the access control devices than the entrance area.
[0015] like Figure 1 As shown, the method includes: S101: Based on the current time, determine the current authorization control mode.
[0016] Generally, two authorization control modes are set up: off-peak authorization control mode and peak authorization control mode. The corresponding peak times are preset, for example, the peak times are set to 7:00-9:00 am and 5:00-7:00 pm. During these times, the peak authorization control mode is used, and at other times, the off-peak authorization control mode is used.
[0017] Of course, holidays and weekdays can also be set. During peak hours on holidays and weekdays, peak hour authorization control mode is used, and during off-peak hours on weekdays, off-peak hour authorization control mode is used.
[0018] S102: If the authorization control mode is the off-peak authorization control mode, the license plate of the designated vehicle to be identified is recognized by the first camera based on the machine vision algorithm, and the access control device is controlled to release the designated vehicle according to the recognition result and the access permission rules. like Figure 2 As shown, in the off-peak authorized control mode, when a designated vehicle enters the vicinity of the first camera, the first camera takes a picture of the designated vehicle, and the license plate is identified by a machine vision license plate recognition system. Here, the machine vision license plate recognition system refers to any system capable of recognizing license plates; any system capable of license plate recognition can be considered the license plate recognition system in this application.
[0019] Specifically, the system defines the cameras and their controlled areas, including configuring and testing the camera numbers, names, locations, and real-time streaming protocols (including the first and second cameras). It supports drawing electronic fences within the camera's monitoring screen and marking the monitoring area for illegal vehicle behavior within the fenced area. For the first camera, the monitored area is the entrance area; for the second camera, the monitored area is the intended entry area.
[0020] The system supports configuration of behavior recognition categories (all-time access vehicles, regular vehicles, area-controlled vehicles, and controlled vehicles) to improve recognition efficiency, reduce missed alarms, and mitigate safety hazards. The research focuses on a vehicle access control module integrating machine vision, behavior recognition, and security alarms, encompassing main video display, camera groups, real-time behavior monitoring, behavior event statistics, and violation capture. The behavior recognition algorithm can utilize YOLO models, CNN models, or other appropriate algorithms. For example, after capturing an image containing a vehicle license plate, the image is preprocessed (including grayscale conversion and image segmentation). Edge detection algorithms such as Sobel and Canny are used to locate the license plate, followed by character segmentation and feature extraction. Character recognition is then performed using a classification algorithm to extract the license plate.
[0021] The alarm logs are displayed in terms of level, status, and visual representation. Event logs are integrated into two main functional modules: alarm overview and alarm list, enabling detailed management of violations. The alarm overview provides statistical analysis of alarm log types and processing status, while the alarm list displays data related to violations, images, and videos, supporting both list and image display modes.
[0022] Of course, a license plate information database can be pre-set to record the license plate information of registered vehicles, and an authorization rule database can be set up to edit various traffic permit rules. The license plate information database is maintained by administrators, including basic functions such as adding, deleting, modifying, and querying information. The license plate information database includes vehicle information (license plate number, vehicle brand and color, etc.) and information of the vehicle owner / user (owner / user's name, contact number, organization or department, etc.).
[0023] Specifically, the traffic permit rules include: special vehicle control rules, area vehicle control rules, and regular vehicle control rules. At this point, the authorization rule base is invoked, and all registered vehicles are categorized by vehicle type within the base.
[0024] The special vehicle management rules allow all-time passage for pre-defined special vehicles. For example, special vehicles can be official vehicles, ambulances, vehicles owned by pregnant women, or vehicles designated for specific events. Vehicles designated as special vehicles are classified as first-class special vehicles. If a vehicle is classified as a first-class special vehicle, a corresponding effective time range is set, and it will revert to a regular vehicle after the effective time range expires. Official vehicles and ambulances are classified as second-class special vehicles, which are usually not registered. Therefore, the authorization rule base can be accessed, and unregistered second-class special vehicles can be set to have no effective time range restriction.
[0025] Area vehicle control rules apply to vehicles operating within predefined areas. These rules are based on whether the access control system's controlled area falls within the permitted passage zone for these vehicles, granting them passage permits. Area-controlled vehicles are those that are subject to certain rules within a designated area, but subject to different rules or are prohibited from entering other areas. Typically, a corresponding effective time range can also be set for this type of vehicle.
[0026] The remaining vehicles are considered regular vehicles. Regular vehicle control rules apply to these vehicles based on whether they are registered and whether odd-even license plate restrictions are in effect, granting them passage permits. For example, registered vehicles are allowed to pass, while unregistered vehicles are not. The odd-even license plate restriction for the day also determines passage. Other rules may also be included, such as allowing vehicles to pass at all times on holidays, or allowing full-time passage from the evening rush hour until 5:00 AM the following day.
[0027] Of course, corresponding control rules can also be set for controlled vehicles (such as vehicles that are temporarily, long-term, or permanently banned from driving due to violations such as speeding, illegal parking, long-term parking, occupying official parking spaces, or traffic accidents), and temporary or long-term bans can be implemented for the recorded vehicles.
[0028] If the detected license plate indicates an unregistered vehicle and it is not classified as a second-class special vehicle, it will not be allowed to pass. If it is a registered vehicle, further investigation will be conducted to determine if it is a vehicle permitted to pass at all times. For example, if it is a first-class special vehicle or a vehicle permitted to pass through an area, the access control will be opened directly to allow passage. If it is not a vehicle permitted to pass at all times, further judgment will be made based on other permission rules in the authorization rule base, such as whether the vehicle complies with the odd-even license plate restriction rule.
[0029] If the conditions are not met, the access control will close, and the vehicle will not be allowed to pass. Alternatively, an alarm can be triggered to inform the driver that passage is denied.
[0030] The alarm system provides license plate number and color information, reasons for refusal, and alarm signals for vehicles without access rights. The license plate number and color information helps management personnel quickly locate the vehicle, the reasons for refusal provide guidance to the driver, and the alarm serves to warn and urge the vehicle to leave. Reasons for refusal include unregistered status, odd-even license plate restrictions, restricted area access, and violations. These reasons can be presented through sound, large-screen text, large-screen graphics, or handheld terminals used by management personnel. Alarm signals include flashing warning lights and audible alerts.
[0031] S103: If the authorization control mode is the peak period authorization control mode, the license plate of the designated vehicle to be identified is recognized based on the second camera and the machine vision algorithm, and the vehicle is guided and persuaded to leave based on the recognition result and the passage permit rules.
[0032] like Figure 3 As shown, when the authorization control mode is the peak period authorization control mode, the license plate recognition is no longer performed directly through the first camera. Since the second camera is located closer to the outside of the control area, vehicle recognition is performed first through the second camera.
[0033] Similarly, the second camera captures the license plate, which is then identified by the license plate recognition system. Based on the license plate information database and the authorization rule database, the system determines whether to allow the vehicle to pass.
[0034] If the vehicle is recognized and allowed to pass, its license plate information can be sent to the server. When the first camera recognizes the license plate information again, it will be allowed to pass directly. If the vehicle is recognized and not allowed to pass, in addition to sounding the alarm, the vehicle can also be asked to leave.
[0035] When persuading designated vehicles to leave, for vehicles in areas that require guidance and persuasion to leave, since they are usually used for passage within fixed areas or for specific activities, they are relatively clear about their own driving range and have a certain understanding of the passage permit rules. Therefore, it is sufficient to guide and persuade these vehicles to leave using the pre-installed guidance equipment.
[0036] For regular vehicles that require guidance and persuasion to leave, since the drivers may not be familiar with the specific traffic permit rules, if the regular vehicle still does not leave after the preset time has been exceeded through the guidance and persuasion device, a prompt will be sent to the management personnel so that the management personnel can go to the regular vehicle to manually guide and persuade it to leave.
[0037] Compared to traditional machine vision-based multi-mode authorization control methods, this approach involves installing independent cameras in the entrance and intended entry areas. These cameras identify vehicles and execute different actions depending on the time and control mode. During peak hours, vehicles can be identified in advance in the intended entry area, allowing unauthorized vehicles to be asked to leave. This prevents vehicles from returning after reaching the entrance and also allows temporarily parked vehicles to be moved, increasing traffic efficiency without reducing the intensity of vehicle control. During off-peak hours, since traffic efficiency is generally high, advance departure is not required to avoid the negative impact of over-control on businesses.
[0038] In one embodiment, when guiding and persuading a designated vehicle to leave, manual persuasion, equipment-based persuasion, or a combination of both can be used. Manual persuasion can involve, after determining that a designated vehicle needs to be guided and persuaded to leave, notifying relevant management personnel to proceed to the second camera location to persuade the vehicle to leave. Equipment-based persuasion can involve setting up appropriate guidance devices (e.g., displays) along the driving path to prompt the vehicle to leave.
[0039] Specifically, the location of the access control equipment and the driving path outside the control area of the access control equipment are first determined. The driving path includes: the entry path and the exit path. The entry path and the exit path may have different settings depending on the actual situation. For example, they may be two lanes side by side, or they may be the same lane, or they may be separate lanes with a connecting path between them.
[0040] At this point, a first camera is positioned on one side of the entry path, within a first preset distance from the access control device (typically within the English entrance area, for example, within 10 meters). At least one second camera is positioned on one side of the entry path, at a distance greater than the first preset distance from the access control device, for example, at a second preset distance (typically greater than the first preset distance, for example, 100 meters). There can be one or more second cameras, positioned within the intended entry area, which is further away than the entrance area.
[0041] Based on this, statistics are compiled on the vehicles that have not been released during the corresponding time of the peak period authorization control mode; among them, vehicles that have not been released are those that have not been released by the access control equipment and have not left along the departure path in the designated vehicles.
[0042] At this point, for each second camera, based on machine vision analysis, the number of vehicles that have not been permitted to proceed from the entry path to the exit path at that second camera location is determined. Machine vision analysis refers to an analysis scheme that can identify vehicle movement trajectories. For each second camera, the number of vehicles making a U-turn and leaving within its vicinity is identified. Only vehicles that made a U-turn at that specific location are counted; vehicles that made a U-turn elsewhere while passing that second camera, or those that reversed away from that second camera, are not included in the count.
[0043] Specifically, the machine vision analysis process here may include: acquiring a video stream containing vehicle motion, obtaining video frames, and performing preprocessing (which may include grayscale conversion, noise reduction, image enhancement, etc.). Background modeling is then performed using frame difference methods, Gaussian mixture models, etc., to extract foreground objects, i.e., vehicles, from the video frames. At this point, based on background differences, target detection is performed to detect and label moving objects (i.e., vehicles) in the video frames.
[0044] The vehicle's trajectory is tracked using target tracking algorithms such as Kalman filtering and multi-object trackers. A U-turn typically involves a vehicle traveling from one direction to another; therefore, the vehicle's trajectory can be analyzed by examining changes in trajectory direction and position to identify the U-turn maneuver. Trajectory direction change detection involves monitoring changes in the vehicle's direction of movement, such as changing from left to right. Position change analysis involves analyzing the vehicle's positional changes during the U-turn, typically manifested as changes in position and angle over a period of time.
[0045] When the changes in trajectory direction and position both conform to the preset changes in U-turn action, the vehicle is considered to have performed a U-turn action, thus completing the machine vision recognition.
[0046] The more vehicles there are, the more vehicles will turn around at this location, making it a more suitable spot for U-turns. Therefore, the second camera with the highest number of vehicles can be retained, and guidance equipment for directing vehicles to turn around can be installed at its location. By using this retained second camera to identify vehicles, when a vehicle is identified as needing to be directed to leave, the nearby guidance equipment will direct it to make a U-turn, thereby increasing traffic efficiency.
[0047] Furthermore, if an intersection area is identified within the driving route, and this intersection area includes at least: an entry path, an exit path, and a connecting path linking the entry and exit paths, then this intersection area is also suitable for U-turns. Therefore, guidance equipment for directing vehicles to turn around is installed at the entry path of the intersection area to increase traffic efficiency when making U-turns.
[0048] It should be noted that the implementation of this application is for a legitimate purpose.
[0049] like Figure 4 As shown in the embodiments of this application, a multi-mode authorization control device based on machine vision is also proposed, including: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a machine vision-based multi-mode authorization control method as described in any of the above embodiments.
[0050] This application also proposes a non-volatile computer storage medium storing computer-executable instructions, characterized in that the computer-executable instructions are configured as: the multi-mode authorization control method based on machine vision described in any of the above embodiments.
[0051] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.
[0052] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.
[0053] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0054] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0057] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0058] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0059] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0060] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0061] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A multi-mode authorization control method based on machine vision, characterized in that, The system is applied in an authorization control system, which includes at least: an access control device installed in the entrance area, a first camera installed at the access control device, and a second camera installed in the intended entry area. The method includes: Based on the current time, determine the current authorization and control mode; If the authorization control mode is the off-peak authorization control mode, the license plate of the designated vehicle to be identified is recognized by the first camera based on the machine vision algorithm, and the access control device is controlled to release the designated vehicle according to the recognition result and the access permission rules. If the authorization control mode is the peak period authorization control mode, the license plate of the designated vehicle to be identified is recognized by the second camera based on the machine vision algorithm, and the vehicle is guided and persuaded to leave based on the recognition result and the passage permit rules. The method further includes: The location of the access control device and the driving path outside the control area of the access control device are determined; the driving path includes: the entry path and the exit path; The first camera is installed on one side of the entry path, at a position within a first preset distance from the access control device; At least one of the second cameras is installed on one side of the entry path, at a location beyond a second preset distance from the access control device; wherein the second preset distance is greater than the first preset distance; The method further includes: The system counts the number of vehicles that have not been released during the corresponding time of the peak period authorization control mode; wherein, the unreleased vehicles refer to those vehicles that have not been released by the access control device and have not left along the departure path among the designated vehicles. For each of the second cameras, based on machine vision analysis, determine the number of vehicles that have not been released from the entry path and have turned around to the exit path at that second camera. The second camera with the highest number of vehicles is retained, and a guidance device for guiding and persuading vehicles to leave is installed at the location of the second camera. The method further includes: Starting from the second preset distance, set multiple statistical points outside the second preset distance; For each statistical point, determine the proportion of vehicles that pass through that statistical point and proceed to the access control equipment for judgment; The statistical points that have a vehicle ratio higher than a preset ratio and whose distance meets the second preset distance requirement are combined to obtain the intended driving area.
2. The method according to claim 1, characterized in that, The method further includes: Identify the intersection areas existing in the driving path; wherein, the intersection areas include at least: the entry path, the exit path, and the connecting path connecting the entry path and the exit path; Guidance devices for guiding and dissuading drivers from entering the intersection area are installed at the entry routes.
3. The method according to claim 1, characterized in that, The traffic permit rules include: special vehicle control rules, regional vehicle control rules, regular vehicle control rules, and controlled vehicle control rules; Among them, the special vehicle control rules are for preset special vehicles, which are allowed to pass at all times; The regional vehicle control rules are for preset regional vehicles. Based on whether the control area of the access control device is within the permitted passage range of the regional vehicles, the rules grant passage permission to the regional vehicles. The controlled vehicle management rules apply to the recorded vehicles and implement temporary or long-term traffic restrictions. The aforementioned regular vehicle control rules apply to the remaining regular vehicles, granting them passage permits based on whether the vehicle is registered and whether odd-even license plate restrictions are in effect.
4. The method according to claim 3, characterized in that, The method further includes: For vehicles in the area that require guidance and persuasion to leave, the pre-installed guidance equipment will be used to guide and persuasion to leave the area. If a regular vehicle that requires guidance and persuasion to leave fails to leave after a preset time has elapsed since being guided and persuaded to leave by the guidance device, a prompt will be sent to the management personnel so that the management personnel can go to the regular vehicle to manually guide and persuade it to leave.
5. The method according to claim 3, characterized in that, The method further includes: Call the authorization rule base and classify all registered vehicles by vehicle type in the authorization rule base; If a vehicle type is classified as a first special vehicle or a regional vehicle, a corresponding effective time range is set, and the vehicle is converted to a regular vehicle after the effective time range is exceeded. The authorization rule base is invoked, and the unregistered second special vehicle is set to have no effective time range restriction in the authorization rule base.
6. A multi-mode authorization and control device based on machine vision, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform, for example, the machine vision-based multi-mode authorization control method as described in any one of claims 1 to 5.
7. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured as: the multi-mode authorization control method based on machine vision as described in any one of claims 1 to 5.