Vehicle fee evasion judgment method and storage medium

Abstract

The invention discloses a vehicle toll evasion judgment method and a storage medium, and the method comprises the steps: recognizing the passing behavior of a target vehicle when the target vehicle runs to a preset region, so as to obtain the vehicle toll evasion confidence of the target vehicle; and sending the vehicle fee evasion confidence of the target vehicle to an arrearage recovery system, so as to judge whether the target vehicle has a fee evasion behavior or not through the arrearage recovery system. According to the embodiment of the invention, a plurality of devices are combined to identify the vehicle fee evasion event and generate the confidence coefficient of fee evasion, so that the arrearage recovery system can judge the reliability of vehicle fee evasion according to the confidence coefficient, and then determine whether the vehicle has a fee evasion behavior or not, thereby improving the recognition capability of a parking lot for the vehicle fee evasion behavior, improving the accuracy of vehicle fee evasion monitoring, and improving the user experience. Parking lot loss is reduced, customer complaints caused by misjudgment are reduced, and user experience is improved.

Description

Technical Field

[0001] The present application relates to the technical field of parking lot management, and in particular to a method for determining vehicle fee evasion and a storage medium. Background Art

[0002] With the continuous increase in the number of vehicles, in order to increase the speed of vehicle traffic, various parking lots generally use unattended fee management solutions, relying on license plate recognition technology to identify vehicles and quickly release them. However, since license plate recognition technology is easily affected by the recognition angle, obstruction by front and rear vehicles, and duplicate license plate travel, the accuracy is not high, resulting in frequent vehicle evasion. Faced with huge traffic and various evasion methods, evasion behavior is becoming increasingly difficult to supervise. And because the current parking lot has a single means of monitoring vehicle evasion, it is easy to mistakenly catch evaders, resulting in a large number of complaints, and has a great impact on the parking lot's revenue. Summary of the invention

[0003] Based on this, it is necessary to provide a vehicle toll evasion judgment method and storage medium to address the above technical issues, so as to solve at least one problem existing in the above-mentioned prior art.

[0004] In a first aspect, a vehicle toll evasion judgment method is provided, which is applied to a vehicle monitoring device, wherein the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine, and the method includes the following steps:

[0005] When the target vehicle travels to the preset area, the traffic behavior of the target vehicle is identified to obtain the vehicle evasion confidence of the target vehicle;

[0006] The vehicle evasion confidence of the target vehicle is sent to the overdue fee collection system, so that the overdue fee collection system can determine whether the target vehicle has evaded the fee.

[0007] In one embodiment, the identifying the traffic behavior of the target vehicle to obtain the toll evasion confidence of the target vehicle includes:

[0008] Identifying the passing behavior of the target vehicle by the image acquisition device to obtain a first target vehicle toll evasion confidence level of the target vehicle; and / or

[0009] Identifying the passing behavior of the target vehicle through the gate ground sensor to obtain a second target vehicle toll evasion confidence level of the target vehicle; and / or

[0010] The control machine identifies the passing behavior of the target vehicle to obtain a third target vehicle toll evasion confidence level of the target vehicle.

[0011] In one embodiment, the vehicle monitoring device includes an image acquisition device and a control machine, and the control machine identifies the traffic behavior of the target vehicle to obtain a third target vehicle toll evasion confidence of the target vehicle, including:

[0012] Tracking the target vehicle by the image acquisition device to determine whether the target vehicle has successfully passed;

[0013] When the target vehicle passes successfully, the image acquisition device sends a vehicle passing event to the control machine;

[0014] The control machine determines whether the target vehicle has paid the fee normally based on the vehicle passing event;

[0015] If the target vehicle fails to pay the toll normally, the control machine generates a third target vehicle toll evasion confidence level of the target vehicle.

[0016] In one embodiment, determining whether the target vehicle has paid the toll normally includes:

[0017] Determine whether an authentication identifier is obtained within a preset time, the authentication identifier indicating that the target vehicle has paid the toll and passed normally;

[0018] If the authentication identifier is obtained within the preset time, the target vehicle can pay the fee and pass normally.

[0019] In one embodiment, the identifying the passing behavior of the target vehicle by the gate ground sensor to obtain a second target vehicle toll evasion confidence of the target vehicle includes:

[0020] Identify whether the target vehicle has a barrier-breaking behavior by using the barrier ground sensor;

[0021] If the target vehicle rushes through the barrier, the confidence level of the second target vehicle evading toll is generated through the barrier ground sensor.

[0022] In one embodiment, the vehicle monitoring device includes an image acquisition device, a control machine, and a barrier ground sensor. After the barrier ground sensor is used to identify whether the target vehicle has a barrier-breaking behavior, the device further includes:

[0023] When the target vehicle rushes through the barrier, a capture instruction is sent to the control machine through the barrier ground sensor;

[0024] The control machine sends the snapshot instruction to the image acquisition device;

[0025] The image acquisition device captures the checkpoint-breaking behavior of the target vehicle based on the capture instruction, so as to obtain the first toll evasion confidence based on the capture result.

[0026] In a second aspect, a method for determining vehicle fee evasion is provided, which is applied to a system for collecting overdue fees, and the method comprises:

[0027] Obtaining a vehicle toll evasion confidence level for a target vehicle identified by a vehicle monitoring device, wherein the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine;

[0028] Based on the vehicle toll evasion confidence level, it is determined whether the target vehicle has toll evasion behavior.

[0029] In one embodiment, determining whether the target vehicle has evaded tolls based on the vehicle evasion confidence level includes:

[0030] Determining a confidence judgment rule based on the traffic scenario of the target vehicle;

[0031] Determining the reliability of toll evasion based on the confidence judgment rule and the confidence of the vehicle toll evasion;

[0032] Based on the toll evasion reliability, it is determined whether the target vehicle has toll evasion behavior.

[0033] In one embodiment, the fare evasion reliability includes high reliability, medium reliability and low reliability, and determining whether the target vehicle has fare evasion behavior based on the fare evasion reliability includes:

[0034] If the toll evasion reliability is high, the target vehicle has toll evasion behavior;

[0035] If the toll evasion reliability is medium, the target vehicle is suspected of toll evasion;

[0036] If the toll evasion reliability is low, then the target vehicle does not have toll evasion behavior.

[0037] In a third aspect, a readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the vehicle toll evasion determination method as described above is implemented.

[0038] The above-mentioned vehicle evasion judgment method and storage medium, the method implementation includes: when the target vehicle travels to the preset area, the passing behavior of the target vehicle is identified to obtain the vehicle evasion confidence of the target vehicle; the vehicle evasion confidence of the target vehicle is sent to the arrears fee collection system, so as to determine whether the target vehicle has evaded the fee through the arrears fee collection system. In the embodiment of the present application, a variety of devices are combined to identify vehicle evasion events and generate evasion confidence, so that the arrears fee collection system can judge the reliability of vehicle evasion based on the confidence, and then determine whether the vehicle has evaded the fee, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints caused by misjudgment, and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 This is a schematic diagram of an application environment of a method for determining vehicle fee evasion in an embodiment of the present application;

[0041] Figure 2 This is a flow chart of a method for determining vehicle evasion in one embodiment of the present application. Figure 1 ;

[0042] Figure 3 This is a flow chart of a vehicle traffic identification method in one embodiment of the present application. Figure 1 ;

[0043] Figure 4 This is a flow chart of a vehicle traffic identification method in one embodiment of the present application. Figure 2 ;

[0044] Figure 5 This is a flow chart of a vehicle traffic identification method in one embodiment of the present application. Figure 3 ;

[0045] Figure 6 This is a flow chart of a method for determining vehicle evasion in one embodiment of the present application. Figure 2 ;

[0046] Figure 7 This is a flow chart of a method for determining vehicle evasion in one embodiment of the present application. Figure 3 ;

[0047] Figure 8This is a schematic diagram of a structure of a vehicle evasion judgment device in an embodiment of the present application. Figure 1 ;

[0048] Fig. 9 This is a schematic diagram of a structure of a vehicle evasion judgment device in an embodiment of the present application. Figure 2 ;

[0049] Fig.10 It is a schematic diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] The vehicle fee evasion judgment method provided in this embodiment can be applied to Figure 1 In the application environment, the vehicle monitoring equipment may include at least one of an image acquisition device, a gate ground sensor and a control machine. The image acquisition device is communicatively connected to the control machine, the gate ground sensor is communicatively connected to the control machine, and the overdue fee collection system may be communicatively connected to the control machine. The image acquisition device is used to collect image data of the vehicle, track and identify the vehicle to obtain information such as vehicle characteristics, vehicle driving direction and vehicle driving trajectory, and based on the information, determine whether the vehicle has following, long-term stay, leaving, entering and other passing behaviors. Based on the passing behavior, if it is abnormal, the abnormal passing sub-event and the confidence of fee evasion can be output. The gate ground sensor can be used to detect whether the vehicle has abnormal behavior, such as rushing through the gate. If so, the abnormal passing sub-event and the confidence of fee evasion can be output. The control machine can determine whether the vehicle has abnormal passing after the vehicle passes, such as passing without paying the fee. If so, the abnormal passing sub-event and the confidence of fee evasion can be output.

[0052] Among them, the overdue payment collection system can be set up locally or in the cloud, and it can be implemented with an independent server or a server cluster composed of multiple servers.

[0053] Embodiment 1

[0054] In one embodiment, if Figure 2 As shown, a method for judging vehicle evasion is provided, which is applied to Figure 1 The vehicle monitoring device shown in the figure includes at least one of an image acquisition device, a gate ground sensor and a control machine, and the method includes the following steps:

[0055] In step S110, when the target vehicle travels to the preset area, the traffic behavior of the target vehicle is identified to obtain the vehicle evasion confidence of the target vehicle;

[0056] Optionally, the image acquisition device can be a super-eye camera, which integrates high-definition imaging technology and intelligent recognition algorithms, can accurately capture multiple frames of continuous target vehicle images, and based on the target vehicle image, identify vehicle information, driving trajectory, driving direction and other information, so as to determine whether the target vehicle passes through the gate, whether it leaves the parking lot, etc. Based on the recognition results, the abnormal traffic event and the confidence of vehicle evasion are output. When the confidence is 1, it means it is credible, and when the confidence is 0, it means it is untrustworthy. If the device does not support the abnormal traffic judgment of this scene, it can be set to not support 2.

[0057] Optionally, the gate ground sensor can be a detection device such as a geomagnetic sensor, an infrared sensor, a lidar sensor, etc., which can be used to detect the lowering of the gate bar of the gate, so as to determine whether the vehicle has abnormal passing behavior, such as rushing through the gate. If there is abnormal passing behavior, an abnormal passing event can be generated, and the corresponding confidence level can be generated. When the confidence level is 1, it means it is credible, and when the confidence level is 0, it means it is untrustworthy. If the device does not support abnormal passing judgment in this scenario, it can be set to not support 2.

[0058] Optionally, the control machine can be a gate control machine, which can be connected to the parking lot management system. When the vehicle passes through, such as leaving the parking lot, if the authentication mark issued by the parking lot management system, that is, the payment notice, is received, it can be determined that the vehicle is passing through normally. If not, an abnormal passage event and the confidence level of the license plate evasion can be generated. When the confidence level is 1, it means it is credible, and when the confidence level is 0, it means it is untrustworthy. If the device does not support the abnormal passage judgment of this scenario, it can be set to not support 2.

[0059] It should be noted that a channel engine module can be set in the control machine, and the channel engine module can be connected to the image acquisition device and the gate ground sensor communication to obtain abnormal passage events and confidence levels identified by the image acquisition device and / or the gate ground sensor and / or abnormal passage events and confidence levels generated by the control machine, and can be assembled to obtain complete abnormal passage events and combined confidence levels, and reported to the overdue fee collection system for the overdue fee collection system to make a fee evasion judgment.

[0060] In step S120, the vehicle evasion confidence of the target vehicle is sent to the overdue fee collection system, so that the overdue fee collection system can determine whether the target vehicle has evaded the fee.

[0061] Optionally, after the overdue fee collection system obtains the abnormal passage event of the target vehicle and the confidence of the vehicle evading the fee, it can obtain the pre-configured confidence judgment rule. Based on the confidence judgment rule and the confidence of the vehicle evading the fee, the reliability is determined. And based on the reliability, it is determined whether the vehicle has evaded the fee. Exemplarily, if the confidence of the control machine is 1, the confidence of the image acquisition device is 1, and the confidence of the gate ground sense is 1, the reliability is high reliability. If the confidence of the control machine is 0, the confidence of the image acquisition device is 0, and the confidence of the gate ground sense is 0, the reliability is low reliability. If the confidence of the control machine is 1, the confidence of the image acquisition device is 1, and the confidence of the gate ground sense is 0, the reliability is high reliability. If the confidence of the control machine is 1, the confidence of the image acquisition device is 1, and the confidence of the gate ground sense is 0, the reliability is medium reliability. Different confidence combinations can correspond to different reliabilities. The details are shown in Table 1 below:

[0062] Table 1: Reliability table corresponding to different confidence combinations in different scenarios

[0063] Scenario Control machine Gate ground sense Image acquisition device Reliability Scenario 1 √ √ √ high Scenario 2 √ √ × high Scene 3 √ × √ high Scene 4 √ × × middle Scene 5 × √ √ high Scene 6 × √ × Low Scene 7 × × √ high Scene 8 × × × Low Scene 9 - √ √ high Scene 10 - √ × Low Scene 11 - × √ high Scene 12 - × × Low Scene 13 √ - √ high Scene 14 √ - × middle Scene 15 × - √ high Scene 16 × - × Low Scene 17 √ √ - high Scene 18 √ × - middle Scene 19 × √ - Low Scene 20 × × - Low

[0064] Among them, "√" indicates confidence level 1, which is credible, "×" indicates confidence level 0, which is untrustworthy, and "-" indicates not supported 2. As can be seen from Table 1 above, the types and quantities of collected confidence levels vary according to different vehicle traffic scenarios, and the devices supporting this scenario are also different, so there may be multiple confidence level combinations. For each confidence level combination, the corresponding scenario can be combined to determine the reliability for vehicle evasion judgment.

[0065] In an embodiment of the present application, a method for determining vehicle evasion is provided, including: when a target vehicle travels to a preset area, identifying the target vehicle's passing behavior to obtain the target vehicle's vehicle evasion confidence; sending the target vehicle's vehicle evasion confidence to an arrears fee collection system, so that the arrears fee collection system can determine whether the target vehicle has evaded the fee. In an embodiment of the present application, a variety of devices are combined to identify vehicle evasion events and generate a confidence level for evasion, so that the arrears fee collection system can determine the reliability of vehicle evasion based on the confidence level, and then determine whether the vehicle has evaded the fee, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints caused by misjudgment, and improve user experience.

[0066] In one embodiment of the present application, the traffic behavior of the target vehicle is identified to obtain the toll evasion confidence of the target vehicle, including:

[0067] Identifying the passing behavior of the target vehicle through an image acquisition device to obtain a first target vehicle toll evasion confidence level of the target vehicle; and / or

[0068] Identify the target vehicle's passing behavior through the gate ground sensor to obtain a second target vehicle toll evasion confidence level of the target vehicle; and / or

[0069] The passing behavior of the target vehicle is identified by the control machine to obtain the third target vehicle toll evasion confidence of the target vehicle.

[0070] Optionally, the image acquisition device may be a super-eye camera or a super-eye camera, which integrates high-definition imaging technology and intelligent recognition algorithms, can accurately capture multiple frames of target vehicle images, and identify vehicle information based on the target vehicle images, such as license plate number, vehicle type, vehicle model, vehicle brand, etc. Then, the target vehicle in each target vehicle image can be tracked by a target tracking algorithm, such as yolo, and based on the position change of the target vehicle in multiple frames of images, the target vehicle's driving trajectory, driving direction and other information can be obtained to determine whether the target vehicle passes through the gate, leaves the parking lot, etc. If the target vehicle passes abnormally, such as following a vehicle, stopping, etc., the first abnormal passage sub-event and the first target vehicle evasion confidence can be output.

[0071] It should be noted that the first abnormal passage sub-event may include the captured target vehicle passage image, target vehicle passage video data within a preset time range, and abnormal passage event type, etc. For example, the image acquisition device may record and retain video data of a preset time period by default, such as 5 minutes of video data. When the abnormal passage behavior of the target vehicle is identified, the video data within a preset time range before and after the time point when the abnormal behavior occurs may be intercepted from the recorded video data, such as video data within 10 seconds before and after.

[0072] The first target vehicle toll evasion confidence can be used to characterize whether the first abnormal passage sub-event is credible. For example, when the confidence is 1, it means it is credible, and when the confidence is 0, it means it is not credible. If the device does not support abnormal passage judgment in this scenario, it can be set to not support 2.

[0073] Optionally, the barrier ground sensor can be used to detect the lowering of the barrier's gate bar, so as to determine whether the vehicle has abnormal passing behavior. Exemplarily, it can detect whether the vehicle is breaking through the barrier, that is, when the gate bar is falling, the target vehicle can trigger the barrier ground sensor, and the barrier opens in the opposite direction. For example, when the target vehicle is following a vehicle, that is, the target vehicle B follows vehicle A to pass, if the barrier is falling, vehicle A has been judged to pass, vehicle B initiates authentication as the current service vehicle, and vehicle B has driven under the barrier. The barrier avoids hitting the vehicle, and when it has not completely fallen, the barrier opens in the opposite direction. At this time, the barrier breaking event can be reported as the second abnormal passage sub-event, and the second target vehicle evasion confidence is generated.

[0074] Among them, the confidence level of the second target vehicle evading toll can be used to characterize whether the second abnormal passage sub-event is credible. For example, when the confidence level is 1, it means it is credible, and when the confidence level is 0, it means it is unreliable. If the device does not support abnormal passage judgment in this scenario, it can be set to not support 2.

[0075] Optionally, the control machine can be a gate control machine, which can be connected to the parking lot management system for communication. After the vehicle passes, within a preset time range, the authentication identifier of the target vehicle sent by the parking lot management system can be obtained. The authentication identifier is used to characterize the normal payment of the vehicle. If the authentication identifier is not obtained, a third abnormal passage event and a third target vehicle evasion confidence level can be generated.

[0076] Among them, the confidence level of the third target vehicle evading toll can be used to characterize whether the third abnormal passage sub-event is credible. For example, when the confidence level is 1, it means it is credible, and when the confidence level is 0, it means it is unreliable. If the device does not support abnormal passage judgment in this scenario, it can be set to not support 2.

[0077] It can be understood that when the target vehicle travels to the preset area, the abnormal passage event and the confidence of evading the fee of the target vehicle can be captured by any one of the image acquisition device, the gate ground sensor and the control machine. Exemplarily, when the target vehicle travels to the location of the gate, the image of the target vehicle passing through the gate can be collected by the image acquisition device, and based on the collected image, it is judged that the vehicle is following the vehicle to evade the fee, and the first confidence of evading the fee of the target vehicle can be generated. At this time, when the target vehicle passes through the gate, the gate ground sensor can detect whether the vehicle has a checkpoint-breaking behavior. If so, it can be determined that the vehicle has a following vehicle to evade the fee, and the second confidence of evading the fee of the target vehicle can be generated. At the same time, when the control machine determines that the vehicle has left the gate, it can determine whether the vehicle has paid the fee. If the fee has not been paid, the third confidence of evading the fee of the target vehicle can be generated. The confidence of evading the fee generated by the image acquisition device, the gate ground sensor or the control machine can be obtained for the target vehicle at the same time. In the scenario before the vehicle enters the barrier, the barrier ground sensor cannot detect the target vehicle at this time. Therefore, there is only the first target vehicle evasion confidence generated by the image acquisition device to determine whether the vehicle has a fake license plate or a following behavior. In the scenario where the vehicle is blocked, for example, when the license plate is blocked by an obstacle or the vehicle in front, the image acquisition device cannot generate the first target vehicle evasion confidence. At this time, the evasion confidence can be generated by the barrier ground sensor and / or the control machine. For example, if the barrier ground sensor fails or is blocked and cannot be detected, the evasion confidence can be generated by the control machine. If the control machine fails, or there is a network delay, etc., the evasion confidence can be generated by the barrier ground sensor. That is, for the target vehicle, it can have the first target vehicle evasion confidence, the second target vehicle evasion confidence, and the third target vehicle evasion confidence, or one of them. The existence of multiple confidences at the same time can improve the reliability of the confidence.

[0078] See also Figure 3 In one embodiment of the present application, the vehicle monitoring device includes an image acquisition device and a control machine, and the control machine identifies the passing behavior of the target vehicle to obtain the third target vehicle evasion confidence of the target vehicle, including:

[0079] In step S210, the target vehicle is tracked by an image acquisition device to determine whether the target vehicle has successfully passed;

[0080] In step S220, when the target vehicle passes successfully, the image acquisition device sends a vehicle passing event to the control machine;

[0081] In step S230, the control machine determines whether the target vehicle has paid the toll and passed normally based on the vehicle passing event;

[0082] In step S240, if the target vehicle fails to pay the toll normally, a third target vehicle toll evasion confidence level of the target vehicle is generated by the control machine.

[0083] Optionally, the image acquisition device can acquire continuous frame images of the target vehicle, and identify and track the target vehicle in each frame image, and connect the position of the target vehicle in each frame image to determine the driving track and driving direction of the target vehicle. By combining the position of the gate, it can be determined whether the target vehicle passes through the gate. If so, a vehicle passage event can be generated. The vehicle passage event can be sent to the control machine. If the control machine receives the authentication identifier sent by the parking management system, it can be determined that the target vehicle has paid the fee normally. If not, it means that the target vehicle has not paid the parking fee. At this time, the third abnormal passage sub-event of the target vehicle and the third target vehicle evasion confidence can be generated. When the confidence is 1, it means it is credible, when the confidence is 0, it means it is untrustworthy, and when the confidence is 2, it means that the device does not support it, that is, the device cannot make abnormal passage judgments.

[0084] It should be noted that the first preset area refers to the preset area range in front and behind the parking lot entrance / exit gate. The image acquisition device may include two, facing the front and rear directions of the exit / entrance gate respectively, so as to capture the entire process of the target vehicle entering and leaving.

[0085] In one embodiment of the present application, determining whether the target vehicle has paid the toll normally includes:

[0086] Determine whether the authentication identifier is obtained within a preset time, and the authentication identifier indicates that the target vehicle has paid the toll and passed normally;

[0087] If the authentication mark is obtained within the preset time, the target vehicle can pay the fee and pass normally.

[0088] Optionally, the control machine and the parking lot management system can be connected in communication. When the vehicle leaves the parking lot, the parking lot management system can send the authentication mark of the vehicle leaving the parking lot to the control machine to indicate that the vehicle has paid for leaving the parking lot normally. If the authentication mark is not received within a preset time, such as within 2 minutes, it means that the vehicle has not paid for leaving the parking lot, and there is a suspicion of evading the fee. It should be noted that the authentication mark can be information such as an identification code and an authentication result. When the vehicle leaves the parking lot, the vehicle can be authenticated, and the vehicle can be paid for leaving the parking lot after the authentication is successful. If the vehicle is not authenticated, there may be situations such as following the vehicle to leave the parking lot or using a fake license plate to leave the parking lot.

[0089] See also Figure 4 In one embodiment of the present application, the passing behavior of the target vehicle is identified by the gate ground sensor to obtain the second target vehicle evasion confidence of the target vehicle, including:

[0090] In step S310, the gate ground sensor is used to identify whether the target vehicle has a barrier-breaking behavior;

[0091] In step S320, if the target vehicle has a checkpoint-breaking behavior, a second target vehicle toll evasion confidence level is generated through the gate ground sensor.

[0092] Optionally, when the target vehicle travels to the second preset area, the target vehicle can trigger the ground sensor of the barrier to determine whether the target vehicle has abnormal traffic behavior, such as rushing through the barrier. Exemplarily, when the target vehicle is following a vehicle, that is, the target vehicle B follows vehicle A to pass, if the barrier is falling, vehicle A has been judged to pass, vehicle B initiates authentication as the current service vehicle, and vehicle B has driven under the barrier. The barrier avoids hitting the vehicle and opens the barrier in the reverse direction before it falls completely. At this time, the rushing through the barrier event can be reported as an abnormal traffic event, and the corresponding confidence level is generated. When the confidence level is 1, it means it is credible, when the confidence level is 0, it means it is untrustworthy, and when the confidence level is 2, it means that the device does not support it, that is, the device cannot make abnormal traffic judgments.

[0093] It should be noted that the gate ground sensor can be set on the gate rod, or can be set around the gate rod, so the second preset area refers to the preset range of the gate rod, such as 1 meter, so as to detect whether the target vehicle drives under the gate rod.

[0094] See also Figure 5 In one embodiment of the present application, the vehicle monitoring device includes an image acquisition device, a control machine, and a gate ground sensor. After the gate ground sensor is used to identify whether the target vehicle has a checkpoint-breaking behavior, the device further includes:

[0095] In step S410, when the target vehicle rushes through the barrier, a capture instruction is sent to the control machine through the barrier ground sensor;

[0096] In step S420, the control machine sends a capture instruction to the image acquisition device;

[0097] In step S430, the image acquisition device captures the target vehicle's checkpoint-breaking behavior based on the capture instruction to obtain a first toll evasion confidence level based on the capture result.

[0098] Optionally, in the scenario of a vehicle rushing through a barrier, if the barrier ground sensor recognizes that the vehicle is rushing through the barrier, a capture instruction is sent to the control machine, so that the capture instruction is forwarded to the image acquisition device through the control machine, and the current communication behavior of the target vehicle is captured by the image acquisition device, and based on the capture result, the first abnormal sub-event and the first evasion confidence can be output, so that the overdue fee collection system can judge the vehicle evasion behavior. In addition, the image acquisition device can send the first abnormal sub-event to the control machine, and the control machine can determine whether the target vehicle is passing through normally with payment. If not, a third abnormal sub-event and a third target vehicle evasion confidence can be generated.

[0099] In the embodiment of the present application, a variety of devices are combined to identify vehicle evasion events and generate a confidence level for evasion, so that the overdue fee collection system can judge the reliability of vehicle evasion based on the confidence level, and then determine whether the vehicle has evaded fees, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints due to misjudgment, and improve user experience.

[0100] Embodiment 2

[0101] In one embodiment, if Figure 6 As shown, a method for judging vehicle evasion is provided, which is applied to Figure 1 The system and method for collecting overdue fees shown in the embodiment include the following steps:

[0102] In step S510, the vehicle evasion confidence level of the target vehicle identified by the vehicle monitoring device is obtained, wherein the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine;

[0103] Optionally, the image acquisition device can be a super-eye camera, which integrates high-definition imaging technology and intelligent recognition algorithms, can accurately capture multiple frames of target vehicle images, and based on the target vehicle images, identify vehicle information, driving trajectory, driving direction and other information, so as to determine whether the target vehicle passes through the gate, whether it leaves the parking lot, etc., and output abnormal traffic events and vehicle evasion confidence based on the recognition results. When the confidence is 1, it means it is credible, when the confidence is 0, it means it is untrustworthy, and when the confidence is 2, it means that the device does not support it, that is, the device cannot make abnormal traffic judgments.

[0104] Optionally, the gate ground sensor can be a detection device such as a geomagnetic sensor, an infrared sensor, a lidar sensor, etc., which can be used to detect the lowering of the gate bar of the gate, so as to determine whether there is abnormal passage of the vehicle. If there is abnormal passage, an abnormal passage event can be generated, and a corresponding confidence level can be generated. When the confidence level is 1, it means it is credible, when the confidence level is 0, it means it is untrustworthy, and when the confidence level is 2, it means that the device does not support it, that is, the device cannot make abnormal passage judgments.

[0105] Optionally, the control machine can be a gate control machine, which can be connected to the parking lot management system. When the vehicle passes through, if the authentication identifier sent by the parking lot management system is received, it can be determined that the vehicle has paid and passed normally. If not, an abnormal passage event and the confidence level of the license plate evasion can be generated. When the confidence level is 1, it means it is credible, when the confidence level is 0, it means it is untrustworthy, and when the confidence level is 2, it means that the device does not support it, that is, the device cannot make abnormal passage judgments.

[0106] It should be noted that a channel engine module can be set in the control machine, and the channel engine module can be connected to the image acquisition device and the gate ground sensor communication to obtain abnormal traffic events and confidence levels identified by the image acquisition device and / or the gate ground sensor and / or abnormal traffic events and confidence levels generated by the control machine, and assemble them into abnormal traffic events and combined confidence levels. The overdue fee collection system can obtain the abnormal traffic events and combined confidence levels after the channel engine module is assembled, so as to determine whether the vehicle has evaded fees.

[0107] In step S520, based on the vehicle evasion confidence level, it is determined whether the target vehicle has evaded tolls;

[0108] Optionally, after obtaining the abnormal passage event of the target vehicle and the confidence of the vehicle evading toll, a pre-configured confidence judgment rule can be obtained, and the reliability can be determined based on the confidence judgment rule, and based on the reliability, it can be determined whether the vehicle has evaded tolls. Exemplarily, if the confidence of the control machine is 1, the confidence of the image acquisition device is 1, and the confidence of the gate ground sense is 1, then the reliability is high reliability. If the confidence of the control machine is 0, the confidence of the image acquisition device is 0, and the confidence of the gate ground sense is 0, then the reliability is low reliability. If the confidence of the control machine is 1, the confidence of the image acquisition device is 1, and the confidence of the gate ground sense is 0, then the reliability is high reliability. If the confidence of the control machine is 1, the confidence of the image acquisition device is 1, and the confidence of the gate ground sense is 0, then the reliability is medium reliability. It can be understood that if the reliability is high, it can be judged that the vehicle has evaded the fee. If the reliability is low, it can be judged that the vehicle has not evaded the fee. If the reliability is medium, it is necessary to further judge whether the vehicle has evaded the fee.

[0109] Optionally, if the target vehicle has evaded fees, the fee can be recovered from the target vehicle. For example, a fee payment reminder can be sent to the user corresponding to the target vehicle, or the target vehicle can be added to a blacklist and sent to each associated parking lot for joint collection, so that when the target vehicle parks again, it will be allowed to enter or exit the parking lot only after paying the parking fee.

[0110] In an embodiment of the present application, a method for determining vehicle evasion is provided, including: obtaining the vehicle evasion confidence of a target vehicle identified by a vehicle monitoring device, wherein the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine; based on the vehicle evasion confidence and abnormal traffic events, determining whether the target vehicle has evaded the fee. In an embodiment of the present application, a variety of devices are combined to identify vehicle evasion events, and the evasion confidence is generated, so that the overdue fee collection system can judge the reliability of vehicle evasion based on the confidence, and then determine whether the vehicle has evaded the fee, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints caused by misjudgment, and improve user experience.

[0111] See also Figure 7 In one embodiment of the present application, based on the vehicle evasion confidence and abnormal traffic events, determining whether the target vehicle has evaded tolls includes:

[0112] In step S610, a confidence judgment rule is determined based on the traffic scenario of the target vehicle;

[0113] In step S620, based on the confidence judgment rule and the confidence of the vehicle evading toll, the reliability of toll evasion is determined;

[0114] In step S630, based on the toll evasion reliability, it is determined whether the target vehicle has toll evasion behavior.

[0115] Optionally, depending on the vehicle traffic scenario, some devices do not support abnormal traffic judgment in this scenario. For example, when the image acquisition device cannot capture images and the barrier ground sensor cannot identify whether the vehicle is breaking through the barrier, it can output "Not Supported 2". Therefore, different confidence combinations can correspond to different reliabilities. For example, for scenario 1, the control machine outputs confidence 1, the barrier ground sensor outputs confidence 1, and the image acquisition device outputs confidence 1, then the reliability is high. For scenario 2, the control machine does not support, the barrier ground sensor outputs confidence 1, and the image acquisition device outputs confidence 1, then the reliability is high. If the barrier ground sensor outputs confidence 0 and the image acquisition device outputs confidence 1. Then the confidence is medium. It can be set specifically according to the actual traffic scenario.

[0116] In one embodiment of the present application, the toll evasion reliability includes high reliability, medium reliability and low reliability. Based on the toll evasion reliability, determining whether the target vehicle has toll evasion behavior includes:

[0117] If the toll evasion reliability is high, the target vehicle has toll evasion behavior;

[0118] If the toll evasion reliability is medium, the target vehicle is suspected of toll evasion;

[0119] If the toll evasion reliability is low, the target vehicle does not have any toll evasion behavior.

[0120] Optionally, if the reliability is high, it can be determined that the vehicle has evaded the fee, if the reliability is low, it can be determined that the vehicle has not evaded the fee, and if the reliability is medium, it can be determined that the vehicle is suspected of evading the fee, and further determination is required to determine whether it has evaded the fee. For example, further determination can be made by manually viewing the vehicle's passage video or payment records.

[0121] In the embodiment of the present application, a variety of devices are combined to identify vehicle evasion events and generate a confidence level for evasion, so that the overdue fee collection system can judge the reliability of vehicle evasion based on the confidence level, and then determine whether the vehicle has evaded fees, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints due to misjudgment, and improve user experience.

[0122] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0123] Embodiment 3

[0124] In one embodiment, a vehicle fare evasion judgment device is provided, and the vehicle fare evasion judgment device corresponds one-to-one to the vehicle fare evasion judgment method in the above-mentioned embodiment 1. Figure 8 As shown, the vehicle fare evasion judgment device is applied to a vehicle monitoring device, and the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine. The vehicle fare evasion judgment device includes an identification unit 101 and a first fare evasion behavior judgment unit 102. The functional modules are described in detail as follows:

[0125] The identification unit 101 is used to identify the traffic behavior of the target vehicle when the target vehicle travels to a preset area, so as to obtain the vehicle evasion confidence of the target vehicle;

[0126] The first fare evasion behavior judgment unit 102 is used to send the vehicle fare evasion confidence of the target vehicle to the overdue payment collection system, so that the overdue payment collection system can judge whether the target vehicle has fare evasion behavior.

[0127] In one embodiment of the present application, the identification unit 101 is further configured to:

[0128] Identifying the passing behavior of the target vehicle through an image acquisition device to obtain a first target vehicle toll evasion confidence level of the target vehicle; and / or

[0129] Identify the target vehicle's passing behavior through the gate ground sensor to obtain a second target vehicle toll evasion confidence level of the target vehicle; and / or

[0130] The passing behavior of the target vehicle is identified by the control machine to obtain the third target vehicle toll evasion confidence of the target vehicle.

[0131] In one embodiment of the present application, the vehicle monitoring device includes an image acquisition device and a control machine, and the recognition unit 101 is also used for:

[0132] Tracking the target vehicle through an image acquisition device to determine whether the target vehicle has successfully passed;

[0133] When the target vehicle passes successfully, the image acquisition device sends a vehicle passing event to the control machine;

[0134] The control machine determines whether the target vehicle has paid the toll normally based on the vehicle passing event;

[0135] If the target vehicle fails to pay the toll normally, a third target vehicle toll evasion confidence level of the target vehicle is generated by the control machine.

[0136] In one embodiment of the present application, the identification unit 101 is further configured to:

[0137] Determine whether the authentication identifier is obtained within a preset time, and the authentication identifier indicates that the target vehicle has paid the toll and passed normally;

[0138] If the authentication mark is obtained within the preset time, the target vehicle can pay the fee and pass normally.

[0139] In one embodiment of the present application, the identification unit 101 is further configured to:

[0140] Identify whether the target vehicle is rushing through the barrier through the barrier ground sensor;

[0141] If the target vehicle rushes through the barrier, the confidence level of the second target vehicle evading the fee is generated through the barrier ground sensor.

[0142] In one embodiment of the present application, the vehicle monitoring device includes an image acquisition device, a control machine, and a gate ground sensor, and the recognition unit 101 is also used for:

[0143] When the target vehicle rushes through the barrier, the gate ground sensor sends a capture command to the control machine;

[0144] The control machine sends the capture instruction to the image acquisition device;

[0145] The image acquisition device captures the target vehicle's checkpoint-breaking behavior based on the capture instruction, so as to obtain a first toll evasion confidence level based on the capture result.

[0146] In the embodiment of the present application, a variety of devices are combined to identify vehicle evasion events and generate a confidence level for evasion, so that the overdue fee collection system can judge the reliability of vehicle evasion based on the confidence level, and then determine whether the vehicle has evaded fees, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints due to misjudgment, and improve user experience.

[0147] Embodiment 4:

[0148] In one embodiment, a vehicle fare evasion judgment device is provided, and the vehicle fare evasion judgment device corresponds one-to-one to the vehicle fare evasion judgment method in the above-mentioned embodiment 2. Fig. 9 As shown, the vehicle fee evasion judgment device is applied to the overdue fee collection system, which includes an abnormal information acquisition unit 201 and a fee evasion judgment unit 202. The functional modules are described in detail as follows:

[0149] The abnormal information acquisition unit 201 is used to obtain the vehicle evasion confidence level of the target vehicle identified by the vehicle monitoring device, wherein the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine;

[0150] The second toll evasion behavior determination unit 202 is used to determine whether the target vehicle has toll evasion behavior based on the toll evasion confidence level of the vehicle.

[0151] In one embodiment of the present application, the second fee evasion behavior determination unit 202 is further configured to:

[0152] Determine the confidence judgment rule based on the traffic scenario of the target vehicle;

[0153] Determine the reliability of toll evasion based on the confidence judgment rules and the confidence of vehicle toll evasion;

[0154] Based on the toll evasion reliability, determine whether the target vehicle has toll evasion behavior.

[0155] In an embodiment of the present application, the evasion reliability includes high reliability, medium reliability and low reliability, and the second evasion behavior determination unit 202 is further configured to:

[0156] If the toll evasion reliability is high, the target vehicle has toll evasion behavior;

[0157] If the toll evasion reliability is medium, the target vehicle is suspected of toll evasion;

[0158] If the toll evasion reliability is low, the target vehicle does not have any toll evasion behavior.

[0159] In the embodiment of the present application, a variety of devices are combined to identify vehicle evasion events and generate a confidence level for evasion, so that the overdue fee collection system can judge the reliability of vehicle evasion based on the confidence level, and then determine whether the vehicle has evaded fees, so as to improve the parking lot's ability to identify vehicle evasion behavior, improve the accuracy of vehicle evasion monitoring, reduce parking lot losses, reduce customer complaints due to misjudgment, and improve user experience.

[0160] The specific definition of the vehicle fare evasion judgment device can be found in the definition of the vehicle fare evasion judgment method mentioned above, which will not be repeated here. Each module in the above-mentioned vehicle fare evasion judgment device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0161] In one embodiment, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for determining vehicle evasion is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0162] In an embodiment of the present application, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the vehicle toll evasion determination method as described above are implemented.

[0163] In an embodiment of the application, a readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the steps of the vehicle evasion determination method as described above are implemented.

[0164] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0165] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0166] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for determining whether a vehicle has evaded tolls, characterized in that: Applied to a vehicle monitoring device, the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine, and the method includes: When the target vehicle travels to the preset area, the traffic behavior of the target vehicle is identified to obtain the vehicle evasion confidence of the target vehicle; The vehicle evasion confidence of the target vehicle is sent to the overdue fee collection system, so that the overdue fee collection system can determine whether the target vehicle has evaded the fee.

2. The vehicle fee evasion determination method according to claim 1, characterized in that: The identifying the passing behavior of the target vehicle to obtain the toll evasion confidence of the target vehicle includes: Identifying the passing behavior of the target vehicle by the image acquisition device to obtain a first target vehicle toll evasion confidence level of the target vehicle; and / or Identifying the passing behavior of the target vehicle through the gate ground sensor to obtain a second target vehicle toll evasion confidence level of the target vehicle; and / or The control machine identifies the passing behavior of the target vehicle to obtain a third target vehicle toll evasion confidence level of the target vehicle.

3. The vehicle fee evasion determination method according to claim 2, characterized in that: The vehicle monitoring device includes an image acquisition device and a control machine, and the control machine identifies the passing behavior of the target vehicle to obtain a third target vehicle toll evasion confidence of the target vehicle, including: Tracking the target vehicle by the image acquisition device to determine whether the target vehicle has successfully passed; When the target vehicle passes successfully, the image acquisition device sends a vehicle passing event to the control machine; The control machine determines whether the target vehicle has paid the fee normally based on the vehicle passing event; If the target vehicle fails to pay the toll normally, the control machine generates a third target vehicle toll evasion confidence level of the target vehicle.

4. The vehicle fee evasion determination method according to claim 3, characterized in that: The determining whether the target vehicle has paid the toll normally includes: Determine whether an authentication identifier is obtained within a preset time, the authentication identifier indicating that the target vehicle has paid the toll and passed normally; If the authentication identifier is obtained within the preset time, the target vehicle can pay the fee and pass normally.

5. The vehicle fee evasion determination method according to claim 2, characterized in that: The identifying the passing behavior of the target vehicle by the gate ground sensor to obtain a second target vehicle toll evasion confidence of the target vehicle includes: Identify whether the target vehicle has a barrier-breaking behavior by using the barrier ground sensor; If the target vehicle rushes through the barrier, the confidence level of the second target vehicle evading toll is generated through the barrier ground sensor.

6. The vehicle fee evasion determination method according to claim 5, characterized in that: The vehicle monitoring device includes an image acquisition device, a control machine and a gate ground sensor. After the gate ground sensor is used to identify whether the target vehicle has a checkpoint-breaking behavior, the device further includes: When the target vehicle rushes through the barrier, a capture instruction is sent to the control machine through the barrier ground sensor; The control machine sends the snapshot instruction to the image acquisition device; The image acquisition device captures the checkpoint-breaking behavior of the target vehicle based on the capture instruction, so as to obtain the first toll evasion confidence based on the capture result.

7. A method for determining whether a vehicle has evaded tolls, characterized in that: Applied to the overdue payment collection system, the method comprises: Obtaining a vehicle toll evasion confidence level for a target vehicle identified by a vehicle monitoring device, wherein the vehicle monitoring device includes at least one of an image acquisition device, a gate ground sensor, and a control machine; Based on the vehicle toll evasion confidence level, it is determined whether the target vehicle has toll evasion behavior.

8. The vehicle fee evasion determination method according to claim 7, characterized in that: The determining, based on the vehicle evasion confidence level, whether the target vehicle has evaded fare, includes: Determining a confidence judgment rule based on the traffic scenario of the target vehicle; Determining the reliability of toll evasion based on the confidence judgment rule and the confidence of the vehicle toll evasion; Based on the toll evasion reliability, it is determined whether the target vehicle has toll evasion behavior.

9. The vehicle fee evasion determination method according to claim 7, characterized in that: The toll evasion reliability includes high reliability, medium reliability and low reliability. The determining whether the target vehicle has toll evasion behavior based on the toll evasion reliability includes: If the toll evasion reliability is high, the target vehicle has toll evasion behavior; If the toll evasion reliability is medium, the target vehicle is suspected of toll evasion; If the toll evasion reliability is low, then the target vehicle does not have toll evasion behavior.

10. A readable storage medium having computer readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the vehicle toll evasion determination method as described in any one of claims 1 to 9 is implemented.

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