Road parking detection method with sentry mode and storage medium

Abstract

The invention discloses a road parking detection method with a sentry mode and a storage medium, and relates to the technical field of intelligent traffic. The method comprises the following steps that: a detection module detects whether an object is close to or far away from detection equipment, and awakens other modules after detecting the object; capturing and detecting an object, and when the detection result is a vehicle, identifying vehicle information and determining a behavior graph of the vehicle; uploading the vehicle information and the alarm information after the camera is shielded; and selectively uploading and locally storing video data within a period of time after the object is detected. According to the technical scheme of the invention, the method can achieve the tracing of the problem that the detection equipment is maliciously shielded or damaged in a parking charging scene, and is higher in application value.

Description

[0001] This application is a divisional application of the Chinese invention patent with application date of "2024.09.25", application number "202411341022.4", and invention name "A road parking detection system and method with sentinel mode". Technical Field

[0002] The present invention relates to the field of intelligent transportation technology, and in particular to a road parking detection method with a sentinel mode and a storage medium. Background Art

[0003] In recent years, with the increase in the number of cars, cars have become an indispensable means of transportation for people's daily travel. The problem of parking difficulties in cities has become more and more serious, especially in busy commercial areas and near public transportation hubs. In order to solve this problem, various regions have carried out intelligent transformation of parking management systems, and improved the turnover rate of urban parking spaces by efficiently managing parking spaces. Among them, there is a battery-powered, self-contained communication module-free wiring intelligent parking management solution. The deployment of this solution does not require construction of the road surface, and the installation is convenient and quick. However, it uses traditional embedded technology, and the startup time of the video acquisition module and the data processing module is greater than 5 seconds. It can only capture the state of the vehicle after it stops, the state of the vehicle leaving, and the state of the vacant parking space. There is a lack of a state map of the vehicle entering the parking space, and it is impossible to form a complete parking evidence chain, which leads to inaccurate parking time detection. In the actual operation process, some car owners will block or destroy the detection equipment to avoid the generation of parking fees, which brings direct economic losses to the parking operation unit. Therefore, the development of a simple and efficient method that can help the intelligent parking management solution master the complete evidence chain is imminent.

[0004] Most of the existing methods that can help intelligent parking management solutions grasp the complete chain of evidence have defects in some aspects. For example, the method based on video inspection equipment uses video inspection equipment to collect parking vehicle information at regular intervals, and manually supplements the evidence chain information. This method is easily affected in recognition rate and work efficiency on busy roads, and is prone to vehicle information missing. Therefore, based on this problem, this application proposes a road parking detection system and method with sentinel mode. Summary of the invention

[0005] Technical Purpose In order to solve the above problems, the purpose of the present invention is to provide a road parking detection method and storage medium with sentinel mode, which not only solves the problem of lack of evidence chain in traditional intelligent parking management solutions, but also realizes the tracing function of the problem of malicious obstruction or damage of detection equipment. The system and method have a wide range of application scenarios, are not easily interfered by external factors, realize the simplification and automation of parking detection, and improve the accuracy of parking space detection.

[0006] Technical Solution In order to achieve the above-mentioned purpose, the present invention provides a road parking detection method and storage medium with sentinel mode, which reduces the wake-up time of the camera module to capture the behavior diagram of the vehicle entering the parking space, thereby forming a complete parking evidence chain, and uploads and locally stores video data within a period of time after the vehicle enters the parking space, thereby realizing the tracing function of the problem that the detection equipment is maliciously blocked or damaged.

[0007] In a first aspect, the present invention provides a road parking detection system with a sentinel mode, comprising: The detection module is used to detect whether an object is approaching or moving away from the detection device, and wake up the other modules after detecting the object; A camera module is used to capture and detect objects, identify vehicle information and determine the behavior diagram of the vehicle when the detection result is a vehicle; Network module, used to upload vehicle information and alarm information when the camera is blocked; Near field communication module, used for data transmission when the detection device cannot connect to the network.

[0008] Furthermore, the system also includes a storage module for storing video data within a period of time after the object is detected.

[0009] Furthermore, the system also includes a fill light module for supplementing light for the detection device.

[0010] Furthermore, the detection module includes at least one of radar detection, infrared sensor detection, and machine vision detection.

[0011] Furthermore, during the operation of the system, except when an object approaches or moves away from the detection device or when the detection device is located in an environment with insufficient light, the detection module is powered for most of the rest of the time to reduce energy consumption, and the normal acquisition of the vehicle entry behavior map is ensured by reducing the wake-up time of the camera module. The sentinel mode system only powers a small number of modules at non-essential times, greatly reducing the energy consumption of the system.

[0012] Further, the method of reducing the wake-up time of the camera module includes: reducing the connection distance between the detection module and the camera module on the hardware circuit PCB board; limiting the function of the camera module at startup, and only pre-enabling the image capture function; improving the sensitivity of the detection module to wake up the camera module as early as possible, and the wake-up speed of the camera module can be as fast as within 200ms. The method enables the camera module to start quickly when the vehicle enters the parking space to capture the behavior diagram of the vehicle entering the parking space, thereby improving the real-time performance of the system.

[0013] Furthermore, the detection module wakes up the camera module to capture and detect the object after detecting that an object is approaching the detection device. When the detection result is a vehicle, the camera module further detects whether the image data contains a license plate, and further identifies the license plate if it contains a license plate.

[0014] Furthermore, the vehicle behavior graph is determined by comprehensively comparing the parking behavior confidence and license plate confidence of multiple frames of the vehicle captured by the camera module, including the vehicle's entry behavior graph, the vehicle's stable stop behavior graph, and the vehicle's exit behavior graph; the vehicle's behavior graph and the parking space vacancy graph after the vehicle exits form a complete parking evidence chain. By capturing the vehicle's entry behavior graph, the system fully records the vehicle's parking process, forming an effective parking evidence chain, which helps resolve parking disputes.

[0015] Furthermore, the parking behavior confidence and the license plate confidence are determined by comprehensively judging conditions such as the clarity of the image, the completeness of the license plate, the change in the size of the license plate, the moving direction of the license plate, etc.

[0016] Furthermore, the vehicle's entry information and the vehicle's status information after it stops are uploaded to the device management platform through the network module at one time; when uploading the alarm information after the camera is blocked, you can manually choose whether to upload the video data after the camera is blocked.

[0017] Furthermore, the storage module automatically stores the video data acquired by the camera module, and marks the video data that is not blocked by the camera when storing; when the video data to be stored exceeds the storage capacity of the storage module, the storage module preferentially overwrites the marked video data with the new blocked video data, and if the marked video data no longer exists, the earliest recorded video data is preferentially overwritten; the size of each video data is about 10M to 20M. By uploading the captured video data to the Internet and storing it locally, it is convenient to quickly and effectively trace back the parking process of the vehicle when a parking dispute occurs; by recording the video data in priority order, data storage is optimized and storage efficiency is improved.

[0018] Furthermore, the near field communication module is used to connect the detection device to an external device contactlessly when the detection device cannot connect to the network, thereby outputting video data stored locally in the detection device, or debugging the detection device and handling device failure problems.

[0019] Furthermore, the fill light module includes a photoresistor and a fill light, wherein the photoresistor is used to measure the light condition of the environment in which the detection device is located, and sends an instruction to enable the fill light when the light is lower than a certain threshold.

[0020] Furthermore, the system is provided with a parking space indicator light to display the current vacancy status of the parking space and the working status of the detection equipment.

[0021] Furthermore, the system displays the location distribution of urban parking spaces in the form of a map through the urban intelligent transportation network solution, providing real-time data for road traffic monitoring and urban traffic planning. It can quickly respond to parking problems by real-time monitoring of the status of on-street parking spaces, effectively allocate and manage parking space resources, provide drivers with fast and accurate parking space information, and reduce the time drivers spend looking for parking spaces.

[0022] Furthermore, the system extracts vehicle and parking space features through a deep residual network solution and provides a more accurate high-level feature representation. The system can extract abstract features in complex environments by capturing spatiotemporal features in image sequences through a residual learning mechanism. By learning the deep features of images, it can more accurately identify the status information of vehicles and parking spaces, thereby improving the accuracy of parking detection; by capturing spatiotemporal features in image sequences, it improves the system's ability to process complex data and reduces the probability of false positives and false negatives.

[0023] In a second aspect, the present invention further provides a road parking detection method with a sentinel mode, the method being based on the system of the first aspect, comprising: The detection module detects whether an object is approaching or moving away from the detection device, and wakes up the other modules after detecting an object; Capture and detect the object, identify the vehicle information and determine the vehicle behavior diagram when the detection result is a vehicle; Upload vehicle information and alarm information when the camera is blocked; Selectively upload and locally store video data within a period of time after an object is detected.

[0024] In a third aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the aforementioned road parking detection method with a sentinel mode is implemented.

[0025] The present invention reduces the wake-up time of the camera module to capture the behavior diagram of the vehicle entering the parking space, thereby forming a complete parking evidence chain; by uploading and locally storing the video data within a period of time after the vehicle enters the parking space, the function of tracing the problem of malicious obstruction or damage of the detection equipment is realized; the location distribution of urban parking spaces is displayed in the form of a map through the urban intelligent transportation network solution, providing real-time data for road traffic monitoring and urban traffic planning; the deep residual network solution is used to extract vehicle and parking space features, and a more accurate high-level feature representation is provided. The residual learning mechanism is used to capture the spatiotemporal features in the image sequence so that the system can extract abstract features in complex environments. The system and method not only solve the problem of the lack of evidence chain in the traditional intelligent parking management solution, but also realize the tracing function of the problem of malicious obstruction or damage of the detection equipment. The system and method have a wide range of application scenarios and are not easily interfered by external factors. The simplification and automation of parking detection are realized, and the accuracy of parking space detection is improved.

[0026] Beneficial Effects By implementing the above-mentioned road parking detection system and method with sentinel mode provided by the present invention, the following technical effects are achieved: (1) The present invention reduces the wake-up time of the camera module to capture the behavior diagram of the vehicle entering the parking space, thereby forming a complete parking evidence chain; when the vehicle enters the parking space, the camera module is quickly started to capture the behavior diagram of the vehicle entering the parking space, thereby improving the real-time performance of the system; by capturing the behavior diagram of the vehicle entering the parking space, the system completely records the parking process of the vehicle, forming an effective parking evidence chain, which is helpful to resolve parking disputes; through the sentinel mode system, only a small number of modules are powered at non-essential times, thereby greatly reducing the energy consumption of the system.

[0027] (2) The present invention realizes the function of tracing the problem of malicious obstruction or damage of detection equipment by uploading and storing the video data within a period of time after the vehicle enters the parking space. It also facilitates the rapid and effective tracing of the parking process of the vehicle when a parking dispute occurs by uploading and storing the captured video data online and locally. It also optimizes data storage and improves storage efficiency by recording video data in priority order.

[0028] (3) The present invention displays the location distribution of urban parking spaces in the form of a map through an urban intelligent transportation network solution, providing real-time data for road traffic monitoring and urban traffic planning; it quickly responds to parking problems by real-time monitoring of the status of on-street parking spaces, can effectively allocate and manage parking space resources, and provide drivers with fast and accurate parking space information, reducing the time drivers spend looking for parking spaces.

[0029] (4) The present invention extracts vehicle and parking space features through a deep residual network solution and provides a more accurate high-level feature representation. It captures the spatiotemporal features in the image sequence through a residual learning mechanism, enabling the system to extract abstract features in complex environments. By learning the deep features of the image, it can more accurately identify the status information of the vehicle and parking space, thereby improving the accuracy of parking detection. By capturing the spatiotemporal features in the image sequence, the system's ability to process complex data is improved, reducing the probability of false alarms and missed alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to make the above-mentioned road parking detection system and method with sentinel mode of the present invention more obvious and easy to understand, the drawings required for use in the specific implementation of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A schematic diagram showing a road parking detection method with a sentinel mode; Figure 2 Schematic diagram showing the effect of the urban intelligent transportation network solution. DETAILED DESCRIPTION

[0032] Embodiment 1: A road parking detection system and method with sentinel mode is provided, wherein the system includes: a detection module, specifically an ultrasonic radar detection module, which is used to detect whether an object is approaching or moving away from the detection device, and wake up the remaining modules after the object is detected; a camera module, which is used to capture and detect objects, and identify vehicle information and determine the vehicle's behavior diagram when the detection result is a vehicle; a network module, which is used to upload vehicle information and alarm information after the camera is blocked; a near field communication module, which is used to transmit data when the detection device cannot connect to the network. Figure 1 shown.

[0033] The road parking detection system with sentinel mode provided by the present invention specifically includes: The system also includes a storage module for storing video data within a period of time after the object is detected.

[0034] The system also includes a supplementary light module, which is used to supplement light for the detection device.

[0035] The detection module includes at least one of radar detection, infrared sensor detection, and machine vision detection.

[0036] During operation, the system only supplies power to the detection module most of the time, except when an object approaches or moves away from the detection device or the detection device is located in an environment with insufficient light. The system also ensures normal acquisition of the vehicle entry behavior map by reducing the wake-up time of the camera module.

[0037] Methods for reducing the wake-up time of the camera module include: reducing the connection distance between the detection module and the camera module on the hardware circuit PCB board; limiting the function of the camera module at startup, and only pre-enabling the image capture function; increasing the sensitivity of the detection module to wake up the camera module as early as possible, and the wake-up speed of the camera module can be as fast as within 200ms.

[0038] Furthermore, the detection module wakes up the camera module to capture and detect the object after detecting that an object is approaching the detection device. When the detection result is a vehicle, the camera module further detects whether the image data contains a license plate, and further identifies the license plate if it contains a license plate.

[0039] The vehicle behavior graph is determined by comprehensively comparing the parking behavior confidence and license plate confidence of multiple frames of the vehicle acquired by the camera module, and includes the vehicle's entry behavior graph, the vehicle's stopping behavior graph, and the vehicle's exit behavior graph; the vehicle's behavior graph and the parking space vacancy graph after the vehicle exits are combined to form a complete parking evidence chain.

[0040] The parking behavior confidence and license plate confidence are determined by comprehensively judging conditions such as the clarity of the image, the completeness of the license plate, the change in the size of the license plate, the moving direction of the license plate, etc.

[0041] The vehicle's entry information and the vehicle's status information after it stops are uploaded to the device management platform through the network module at one time; when uploading the alarm information after the camera is blocked, you can manually choose whether to upload the video data after the camera is blocked.

[0042] Furthermore, the storage module automatically stores the video data acquired by the camera module, and marks the video data that is not obstructed by the camera during storage; when the video data that needs to be stored exceeds the storage capacity of the storage module, the storage module preferentially overwrites the new obstructed video data on the marked video data, and if the marked video data no longer exists, it preferentially overwrites the video data with the earliest time record; the size of each video data is approximately 10M to 20M.

[0043] Furthermore, the near field communication module is used to connect the detection device to an external device contactlessly when the detection device cannot connect to the network, thereby outputting video data stored locally in the detection device, or debugging the detection device and handling device failure problems.

[0044] The fill light module includes a photoresistor and a fill light. The photoresistor is used to measure the light conditions of the environment in which the detection device is located, and sends an instruction to enable the fill light when the light is lower than a certain threshold.

[0045] The system is provided with a parking space indicator light to display the current vacancy status of the parking space and the working status of the detection equipment.

[0046] The system uses the city intelligent transportation network solution to display the location distribution of urban parking spaces in the form of a map, providing real-time data for road traffic monitoring and urban traffic planning, including: matching the data identified by the road parking detection system with the geographic information in the city intelligent transportation network solution database, which contains the geographic coordinates, belonging area, type and other information of the parking spaces; combining the real-time collected data with the city intelligent transportation network solution, and performing spatial analysis and processing, using the city intelligent transportation network solution to monitor and manage vehicle locations and parking space status in real time; and displaying parking space status, vehicle location and related statistical information in the form of a map. The effect of the city intelligent transportation network solution is as follows: Figure 2 shown.

[0047] Embodiment 2: On the basis of the above-mentioned embodiments, a deep residual network solution is added to extract vehicle and parking space features and provide more accurate high-level feature representation. The spatial and temporal features in the image sequence are captured through the residual learning mechanism, so that the system can extract abstract features in complex environments. Among them, the video data needs to be preprocessed to meet the input requirements of the deep learning model. The normalization formula is as follows:

[0048] in, Represents raw data, such as the pixel values ​​of an image; represents the mean of the data; Indicates the standard deviation of the data; represents the normalized data; Represents the scaling factor, which is used to control the scaling degree of the normalized data.

[0049] ResNet is selected as the basic model, and its residual block is used to improve the efficiency of training deep networks. The design formula of the residual block is expressed as:

[0050] in, Indicates The input of the layer; Indicates The output of the layer; Represents the residual function, which represents the operation inside the residual block; Indicates The weight parameters of the layer; Represents the adjustment factor, which is used to control the influence of the residual function on the output.

[0051] The key features in the video are extracted through the trained ResNet model. The feature extraction process is as follows:

[0052] in, represents the feature map, which is the output of the activation function; Represents input data; represents the residual block function with weights; represents the weight parameter; represents the parameterized rectified linear unit activation function, which is used to increase nonlinear characteristics; Represents the parameters of the activation function, which are used to control the strength of the nonlinearity.

[0053] Parking behavior analysis is performed through the extracted features and implemented through the following decision function:

[0054] in, Represents the predicted output, i.e., the category probability distribution; represents the weight of the fully connected layer; represents the bias of the fully connected layer; Represents the feature map extracted from ResNet; Represents the activation function, which is used in the output layer of multi-classification problems to convert the output into a probability distribution; Represents the weight decay term, which is used to adjust The confidence level of the output.

[0055] The output formula of the system is:

[0056] in, Indicates the final action instruction, such as whether to send an alarm; represents the weight of the output layer; represents the bias of the output layer; Represents the output of the previous layer; express The activation function is used in the output layer of the binary classification problem to convert the output into a probability value between 0 and 1; Represents the threshold parameter.

[0057] For example, suppose there is an image data pixel value , the mean value of the pixel and standard deviation , select :

[0058] Assume that the residual function Through a simple operation ,and , select To balance the residual and the original input:

[0059] Select As parameters of the parameterized activation function:

[0060] Assumptions , , select As a weight decay term:

[0061] Assumptions , , select As threshold parameter:

[0062] The effect of the deep residual network solution is shown in Table 1: Table 1. Summary of the effects of deep residual network solutions

[0063] As shown in Table 1, the model’s accurate recognition rate after optimization by the deep residual network scheme is further improved, the false alarm rate and missed alarm rate are significantly reduced, and the response time is also reduced. This shows that the deep residual network scheme can significantly improve the accuracy of the road parking detection method with sentinel mode.

[0064] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable non-transient storage media containing computer-usable program code.

[0065] The present invention can provide computer program instructions to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the system.

[0066] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions of the system.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions of the system.

Claims

1. A road parking detection method system with sentinel mode, characterized in that: include: The detection module is used to detect whether an object is approaching or moving away from the detection device, and wake up the other modules after detecting the object; A camera module, used to capture and detect objects, identify vehicle information and determine the vehicle's behavior map when the detection result is a vehicle; Network module, used to upload vehicle information and alarm information when the camera is blocked; Near field communication module, used for data transmission when the detection device cannot connect to the network.

2. The road parking detection system with sentinel mode according to claim 1, characterized in that: During operation, the system only supplies power to the detection module most of the time, except when an object approaches or moves away from the detection device or the detection device is located in an environment with insufficient light. The system also ensures normal acquisition of the vehicle entry behavior map by reducing the wake-up time of the camera module.

3. The road parking detection system with sentinel mode according to claim 1 or 2, characterized in that: The vehicle behavior graph is determined by comprehensively comparing the parking behavior confidence and license plate confidence of multiple frames of the vehicle acquired by the camera module, and includes the vehicle's entry behavior graph, the vehicle's stopping behavior graph, and the vehicle's exit behavior graph; the vehicle's behavior graph and the parking space vacancy graph after the vehicle exits are combined to form a complete parking evidence chain.

4. The road parking detection system with sentinel mode according to claim 1, characterized in that: The vehicle's entry information and the vehicle's status information after it stops are uploaded to the device management platform through the network module at one time; when uploading the alarm information after the camera is blocked, you can manually choose whether to upload the video data after the camera is blocked.

5. The road parking detection system with sentinel mode according to claim 1, characterized in that: The system also includes a storage module for storing video data within a period of time after the object is detected. The storage module automatically stores the video data acquired by the camera module and marks the video data that is not obstructed by the camera during storage. When the video data that needs to be stored exceeds the storage capacity of the storage module, the storage module preferentially overwrites the new obstructed video data on the marked video data. If the marked video data no longer exists, the storage module preferentially overwrites the video data with the earliest time record.

6. The road parking detection system with sentinel mode according to claim 1 or 5, characterized in that: The system also includes a fill light module for supplementing light for the detection device. The fill light module includes a photoresistor and a fill light. The photoresistor is used to measure the light conditions of the environment in which the detection device is located, and sends an instruction to enable the fill light when the light is lower than a certain threshold.

7. The road parking detection system with sentinel mode according to claim 1, characterized in that: The system displays the location distribution of urban parking spaces in the form of a map through an urban intelligent transportation network solution, and provides real-time data for road traffic monitoring and urban traffic planning, including: matching the data identified by the road parking detection system with the geographic information in the urban intelligent transportation network solution database, wherein the urban intelligent transportation network solution database contains information such as the geographic coordinates, belonging area, and type of the parking spaces; combining the real-time collected data with the urban intelligent transportation network solution, and performing spatial analysis and processing, and using the urban intelligent transportation network solution to monitor and manage the vehicle location and parking space status in real time; and displaying the parking space status, vehicle location, and related statistical information in the form of a map.

8. The road parking detection system with sentinel mode according to claim 1, characterized in that: The system extracts vehicle and parking space features through a deep residual network solution and provides a more accurate high-level feature representation. The system captures the spatiotemporal features in the image sequence through the residual learning mechanism, enabling the system to extract abstract features in complex environments. The video data needs to be preprocessed to meet the input requirements of the deep learning model. The normalization formula is as follows: ,in, Represents the original data; represents the mean of the data; Indicates the standard deviation of the data; represents the normalized data; Represents the scaling factor, which is used to control the scaling degree of the normalized data; ResNet is selected as the basic model, and its residual block is used to improve the efficiency of training deep networks. The design formula of the residual block is expressed as: ,in, Indicates The input of the layer; Indicates The output of the layer; Represents the residual function, which represents the operation inside the residual block; Indicates The weight parameters of the layer; Represents the adjustment factor, which is used to control the influence of the residual function on the output; The key features in the video are extracted through the trained ResNet model. The feature extraction process is as follows: ,in, represents the feature map, which is the output of the activation function; Represents input data; represents the residual block function with weights; represents the weight parameter; represents the parameterized rectified linear unit activation function, which is used to increase nonlinear characteristics; Represents the parameters of the activation function, which is used to control the strength of nonlinearity; Parking behavior analysis is performed through the extracted features and implemented through the following decision function: ,in, Represents the predicted output, i.e., the category probability distribution; represents the weight of the fully connected layer; represents the bias of the fully connected layer; Represents the feature map extracted from ResNet; Represents the activation function, which is used in the output layer of multi-classification problems to convert the output into a probability distribution; Represents the weight decay term, which is used to adjust Confidence of the output; The output formula of the system is: ,in, Indicates the final action instruction; represents the weight of the output layer; represents the bias of the output layer; Represents the output of the previous layer; express The activation function is used in the output layer of the binary classification problem to convert the output into a probability value between 0 and 1; Represents the threshold parameter.

9. A road parking detection method with sentinel mode, characterized in that: The method is implemented based on the system according to any one of claims 1 to 8: The method comprises: The detection module detects whether an object is approaching or moving away from the detection device, and wakes up the other modules after detecting an object; Capture and detect the object, identify the vehicle information and determine the vehicle behavior diagram when the detection result is a vehicle; Upload vehicle information and alarm information when the camera is blocked; Selectively upload and locally store video data within a period of time after an object is detected.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: When the computer program is executed, the road parking detection method with sentinel mode as claimed in claim 9 is implemented.

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