Vehicle Tracking Method, Device, and Storage Medium

Abstract

The present invention discloses a method for tracking a vehicle, a computer device, and a storage medium. The method for tracking a vehicle includes steps of obtaining detection section data and detection area data, obtaining identification information of a target vehicle detection device, and querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle. By obtaining the detection section data, the present invention can obtain the detection range of a single vehicle detection device; by obtaining the detection area data, the present invention can obtain multiple detection areas, which is equivalent to forming a query unit with the detection ranges of multiple vehicle detection devices to query or predict the position of the target vehicle, and can improve the tracking efficiency of the position of the target vehicle. The present invention can achieve the tracking of the target vehicle without relying on manual methods such as driving for tracking and interception, or arranging personnel to stay on site for waiting. The present invention is widely applied to the technical field of traffic control.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and in particular, to a method for tracking vehicles, a computer device, and a storage medium. Background Art

[0002] In the case where real-time positioning data of a vehicle can be obtained, the vehicle can be tracked based on the positioning data. However, in the case where the vehicle cannot obtain positioning data or cannot be contacted, etc., the technology for tracking the vehicle based on positioning data will become unavailable, and it is necessary to rely on manual methods such as driving to track and intercept, arranging personnel to stay at a fixed point, etc. to track the vehicle, which will face defects such as a high error rate brought by manual methods, and even affect traffic safety. Summary of the Invention

[0003] Aiming at at least one technical problem such as the inability to track a vehicle when positioning data cannot be obtained, the purpose of the present invention is to provide a method for tracking vehicles, a computer device, and a storage medium.

[0004] On the one hand, an embodiment of the present invention includes a method for tracking vehicles, comprising:

[0005] Obtain detection section data; the detection section data records the identification information of a plurality of vehicle detection devices and the azimuth information of the detection ranges of each of the vehicle detection devices;

[0006] Obtain detection area data; the detection area data records the identification information of a plurality of combinations of vehicle detection devices; each combination of vehicle detection devices includes a plurality of vehicle detection devices, and the detection ranges of the vehicle detection devices in the same combination of vehicle detection devices are used as boundaries to enclose a detection area;

[0007] Determine a target vehicle detection device; the target vehicle detection device is a vehicle detection device that detects a target vehicle;

[0008] Obtain the identification information of the target vehicle detection device;

[0009] Query in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle.

[0010] Further, the querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device so as to track the position of the target vehicle includes:

[0011] When the target vehicle detection device detects the target vehicle in real time currently, query in the detection section data according to the identification information of the target vehicle detection device to determine the detection range where the target vehicle is located;

[0012] Determine the current position of the target vehicle according to the detection range where the target vehicle is located.

[0013] Further, the querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device to track the position of the target vehicle further includes:

[0014] When the target vehicle detection device detects the target vehicle in real time currently, query in the detection area data according to the identification information of the target vehicle detection device to determine the detection area where the target vehicle is located;

[0015] Predict the position of the target vehicle according to the detection range and detection area where the target vehicle is located.

[0016] Further, the predicting the position of the target vehicle according to the detection range and detection area where the target vehicle is located includes:

[0017] Determine the detection area where the target vehicle is located as the first detection area;

[0018] Obtain the boundary direction of the first detection area;

[0019] Starting from the current position of the target vehicle, determine the internal position of the first detection area pointed to by the boundary direction;

[0020] Use the internal position as the prediction result of the position of the target vehicle.

[0021] Further, the predicting the position of the target vehicle according to the detection range and detection area where the target vehicle is located includes:

[0022] Determine the detection area where the target vehicle is located as the first detection area;

[0023] Obtain the boundary direction of the first detection area;

[0024] Determine the cumulative moving distance of the target vehicle within the first detection area;

[0025] When the cumulative moving distance reaches a preset threshold, starting from the current position of the target vehicle, determine the second detection area pointed to by the boundary direction;

[0026] Use the second detection area as the prediction result of the position of the target vehicle.

[0027] Further, the querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device to track the position of the target vehicle includes:

[0028] When the target vehicle detection device detected the target vehicle in a historical period, query in the detection area data according to the identification information of the target vehicle detection device to determine the detection area where the target vehicle may be currently located.

[0029] Further, the querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device to track the position of the target vehicle further includes:

[0030] Determine the cumulative duration for which the target vehicle detection device detected the target vehicle;

[0031] Determine the interval duration from the last detection of the target vehicle by the target vehicle detection device to the current time;

[0032] According to the cumulative duration and the interval duration, determine the likelihood of the target vehicle being in the detection area.

[0033] Further, the likelihood is positively correlated with the cumulative duration and negatively correlated with the interval duration.

[0034] On the other hand, an embodiment of the present invention further includes a computer device, including a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to execute the method for tracking a vehicle in the embodiment.

[0035] On the other hand, an embodiment of the present invention further includes a storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to execute the method for tracking a vehicle in the embodiment when executed by the processor.

[0036] The beneficial effects of the present invention are as follows: In the vehicle tracking method in the embodiment, by obtaining the detection road section data, the detection range of a single vehicle detection device can be obtained, and the position of the target vehicle can be queried or the position of the target vehicle can be predicted according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle; by obtaining the detection area data, multiple detection areas can be obtained, and each detection area is surrounded by the detection ranges of multiple vehicle detection devices. The position of the target vehicle can be queried or the position of the target vehicle can be predicted according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle. It is equivalent to forming a query unit with the detection ranges of multiple vehicle detection devices to query or predict the position of the target vehicle, which can reduce the number of vehicle detection devices to be queried and expand the size of the detection range for each query, improving the tracking efficiency of the position of the target vehicle; the vehicle tracking method in the embodiment only needs to use the detection data of the vehicle detection device to realize the tracking process of the target vehicle, and can realize the tracking of the target vehicle without relying on artificial methods such as driving tracking and interception and arranging personnel to stay on site when the positioning data of the vehicle itself cannot be obtained, with high efficiency, low error rate, little impact on traffic order and high safety level. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flowchart of the vehicle tracking method in the embodiment;

[0038] Figure 2 is a schematic diagram of the detection range of the vehicle detection device in the embodiment;

[0039] Figure 3 is a schematic diagram of the detection area formed by multiple vehicle detection devices in the embodiment;

[0040] Figure 4 is a schematic diagram of the boundary direction of the detection area in the embodiment;

[0041] Figure 5 is a schematic diagram of the principle of the step of determining the cumulative moving distance of the target vehicle in the first detection area in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In this embodiment, with reference to Figure 1 , the vehicle tracking method includes the following steps:

[0043] S1. Obtain the detection road section data; the detection road section data records the identification information of multiple vehicle detection devices and the azimuth information of the detection ranges of each vehicle detection device;

[0044] S2. Obtain detection area data; the detection area data records the identification information of multiple combinations of vehicle detection devices; each combination of vehicle detection devices includes multiple vehicle detection devices, and the detection ranges of the vehicle detection devices in the same combination of vehicle detection devices are used as boundaries to enclose a detection area;

[0045] S3. Determine the target vehicle detection device; the target vehicle detection device is the vehicle detection device that detects the target vehicle;

[0046] S4. Obtain the identification information of the target vehicle detection device;

[0047] S5. Query in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle.

[0048] In this embodiment, steps S1 - S5 can be executed by the background server. The background server can be connected to each vehicle detection device through the Internet or a dedicated network.

[0049] In this embodiment, the vehicle detection device can be a bayonet monitoring system, specifically a camera. Each vehicle detection device has its unique number as the identification information. When the vehicle detection device communicates externally, by sending or receiving its corresponding identification information, external devices can identify and distinguish the vehicle detection device.

[0050] The vehicle detection device is installed at positions such as road intersections. The detection range of the vehicle detection device, that is, the direction of its field of view, can be the same as the extension direction of the road, and the field of view can be concentrated in one lane of the road. The vehicle detection device captures the license plate or the whole vehicle image of the target vehicle in its detection range, and identifies the specific identity of the target vehicle included in the captured image by identifying the license plate or extracting features from the whole vehicle image. Since the vehicle detection device is installed at a fixed position and its detection range is also at a fixed position, the target vehicle detected by the vehicle detection device is at the fixed position where the detection range of the vehicle detection device is located when it is detected. By analyzing the surrounding environment of the whole vehicle image captured by the vehicle detection device, the specific position of the target vehicle when it is detected can be further determined.

[0051] The process of identifying the identity and specific position of the target vehicle by the vehicle detection device can be carried out through manual observation or through image analysis algorithms such as artificial intelligence.

[0052] In step S1, after the vehicle detection device is installed and calibrated, the orientation of the detection range of the vehicle detection device is determined. The calibration record of the vehicle detection device can be obtained, so that according to the identification information of a vehicle detection device, the orientation information of the detection range of this vehicle detection device can be queried. The calibration record is converted into detection section data. In this embodiment, a format of the detection section data is shown in Table 1.

[0053] Table 1 Detection Section Data

[0054]

[0055] Referring to Table 1, the detection section data includes the identification information (device number) of the vehicle detection device. According to the identification information of the vehicle detection device, information such as the detection direction of a vehicle detection device, the lane number to be detected, the name of the downstream intersection and the downstream intersection direction faced by the vehicle detection device can be determined. These information belong to the orientation information of the detection range of the vehicle detection device. According to these information, the orientation of the detection range of the vehicle detection device can be determined.

[0056] Referring to Figure 2 , for a vehicle detection device A set at an intersection, according to the detection section data, the detection range of the vehicle detection device A can be determined, as shown by the range enclosed by the two dotted lines drawn from the dot A in Figure 2 .

[0057] In step S2, the background server can analyze the detection section data and identify each combination of vehicle detection devices that meet the conditions. Each combination of vehicle detection devices includes multiple vehicle detection devices. For the vehicle detection devices in the same combination of vehicle detection devices, if their detection ranges are used as boundaries, a detection area can be enclosed. For example, Figure 3 , in which the dot A representing the vehicle detection device A, the dot B representing the vehicle detection device B, the dot C representing the vehicle detection device C, and the dot D representing the vehicle detection device D, their detection ranges are respectively shown by the ranges enclosed by the two dotted lines drawn from the dots. Using the detection ranges of the vehicle detection device A, the vehicle detection device B, the vehicle detection device C, and the vehicle detection device D as boundaries, a detection area ABCD can be enclosed. If the detection range of a vehicle detection device is not large enough to include another vehicle detection device, for example Figure 3 in which the vehicle detection device B may not enter the detection range of the vehicle detection device A, then the extension line (or extended area) of the detection range of the vehicle detection device can be used as the boundary. For example, in Figure 3After the detection range of the vehicle detection device A is extended, it can reach the location of the vehicle detection device B, so that the vehicle detection device A and the vehicle detection device B can be connected, becoming one of the boundaries of the detection area ABCD.

[0058] In this embodiment, a format of the detection area data is shown in Table 2. It can be seen from Table 2 that some contents of the detection area data are the same as those of the detection road section data. The detection area data records the names of the boundary roads of the detection areas corresponding to the vehicle detection devices. Several vehicle detection devices corresponding to the same boundary road name of the detection area belong to the same vehicle detection device combination. Therefore, based on the detection road section data, by marking which vehicle detection device combination each vehicle detection device belongs to, the detection area data can be obtained.

[0059] Table 2 Detection Area Data

[0060]

[0061] In step S3, the background server receives the detection data sent by each vehicle detection device in real time and parses it to monitor whether there is a vehicle detection device that has detected a target vehicle within its detection range. If the current background server does not find any vehicle detection device that has detected a target vehicle in real time, then the background server can read the target vehicles detected by the vehicle detection devices in the historical time period from the database. The target vehicle detection device refers to the vehicle detection device that has detected the target vehicle. In different specific embodiments, the target vehicle detection device refers to the vehicle detection device that has currently detected the target vehicle, or in the case where there is no vehicle detection device that has currently detected the target vehicle, the target vehicle detection device refers to the vehicle detection device that has detected the target vehicle in the historical time period.

[0062] In step S4, for the vehicle detection device that has detected the target vehicle, that is, the target vehicle detection device, the background server obtains the identification information of the target vehicle detection device.

[0063] In step S5, the background server queries in the detection road section data obtained in step S1 and the detection area data obtained in step S2 according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle.

[0064] When the background server executes step S5, that is, according to the identification information of the target vehicle detection device, queries in the detection road section data and the detection area data to track the position of the target vehicle, the following steps can be executed:

[0065] S501A. When the target vehicle detection device detects a target vehicle in real time currently, query in the detection section data according to the identification information of the target vehicle detection device to determine the detection range where the target vehicle is located;

[0066] S502A. Determine the current position of the target vehicle according to the detection range where the target vehicle is located.

[0067] In step S501A, if the target vehicle detection device detects a target vehicle in real time currently, it means that the target vehicle appears within the detection range of the target vehicle detection device. The background server queries in the detection section data shown in Table 1 according to the identification information of the target vehicle detection device, and the background server can determine the detection range where the target vehicle is located. In step S502A, the background server can determine on which road, which lane and in which direction the target vehicle is traveling, etc., so as to realize the tracking of the target vehicle.

[0068] When the background server executes step S5, that is, according to the identification information of the target vehicle detection device, query in the detection section data and the detection area data to track the position of the target vehicle, the following steps can also be executed:

[0069] S501B. When the target vehicle detection device detects a target vehicle in real time currently, query in the detection area data according to the identification information of the target vehicle detection device to determine the detection area where the target vehicle is located;

[0070] S502B. Predict the position of the target vehicle according to the detection range and the detection area where the target vehicle is located.

[0071] The principle of step S501B is the same as that of step S501A. If the target vehicle detection device detects a target vehicle in real time currently, it means that the target vehicle appears within the detection range of the target vehicle detection device. The background server queries in the detection section data shown in Table 1 according to the identification information of the target vehicle detection device, and the background server can determine the detection range where the target vehicle is located.

[0072] In step S502B, the background server can use two methods to predict the position of the target vehicle according to the detection range and the detection area where the target vehicle is located. One of the prediction methods includes the following steps:

[0073] S50201. Determine that the detection area where the target vehicle is located is the first detection area;

[0074] S50202. Obtain the boundary direction of the first detection area;

[0075] S50203. Starting from the current position of the target vehicle, determine the internal position of the first detection area pointed by the boundary direction;

[0076] S50204. Use the internal position as the prediction result of the position of the target vehicle.

[0077] In step S50201, assume that the detection area where the target vehicle is currently located is a specific detection area, that is, the first detection area. In this embodiment, the vehicle detection device A in Figure 4 can be used as an example, and the first detection area is the area ABCD in Figure 4 for illustration.

[0078] In step S50202, the background server obtains the boundary direction of the first detection area, that is, the clockwise direction of A→B→C→D shown in Figure 4

[0079] In step S50203, the background server has determined that the detection area where the target vehicle is located is the first detection area, that is, the area ABCD in Figure 4 Figure 4 Figure 4 Starting from the current position of the target vehicle shown in

[0080] and moving along the boundary direction, determine the internal position of the first detection area pointed by the boundary direction. In

[0081] S50204, the internal position of the first detection area pointed by the boundary direction includes the detection range of vehicle detection device B, the detection range of vehicle detection device C, and the detection range of vehicle detection device D. Therefore, in step S50204, it can be predicted that the position of the target vehicle will move to the detection range of vehicle detection device B (the road between vehicle detection device B and vehicle detection device C), the detection range of vehicle detection device C (the road between vehicle detection device C and vehicle detection device D), and vehicle detection device D (the road between vehicle detection device D and vehicle detection device A), thus completing the prediction of the position of the target vehicle.

[0082] Another prediction method for the background server to predict the position of the target vehicle includes the following steps:

[0083] S50205. Determine that the detection area where the target vehicle is located is the first detection area;

[0084] S50208. When the cumulative moving distance reaches a preset threshold, starting from the current position of the target vehicle, determine the second detection area pointed by the boundary direction;

[0085] S50209. Use the second detection area as the prediction result of the position of the target vehicle.

[0086] The principle of step S50205 is the same as that of S50201, and the principle of step S50206 is the same as that of S50202. In step S50207, referring to Figure 5 , the background server samples the position of the target vehicle at different times such as t1 and t2 through various vehicle detection devices, so as to obtain the moving trajectory of the target vehicle. By calculating the length of the moving trajectory, the cumulative moving distance of the target vehicle within the first detection area can be obtained.

[0087] In step S50208, a threshold can be set. For example, the threshold is set to half of the sum of the boundary lengths of the first detection area ABCD. When the cumulative moving distance of the target vehicle within the first detection area reaches the preset threshold, that is, half of the sum of the boundary lengths of the first detection area ABCD, it can be considered that the target vehicle has moved a sufficient distance within the first detection area. According to the conventional driving habit, the target vehicle will drive out of the first detection area ABCD.

[0088] Assume that step S50208 is executed at time t2, then the current position of the target vehicle is as Figure 5 shown within the detection range of vehicle detection device B, and the corresponding boundary direction points to Figure 5 the lower part. Assume Figure 5 there is a second detection area in the lower part, then in step S50209, it can be predicted that the target vehicle will drive towards the second detection area after driving out of the first detection area ABCD, thus completing the prediction of the position of the target vehicle.

[0089] When the background server executes step S5, that is, according to the identification information of the target vehicle detection device, query in the detection section data and the detection area data to track the position of the target vehicle, the following steps can be executed:

[0090] S501C. When the target vehicle detection device has detected the target vehicle in a historical period, query in the detection area data according to the identification information of the target vehicle detection device to determine the detection area where the target vehicle may be currently located.

[0091] The prerequisite for executing step S501C may be that due to reasons such as the target vehicle driving out of the detection areas of all vehicle detection devices, the background server does not find that any vehicle detection device has detected the target vehicle in real time at the current moment. The background server selects a vehicle detection device as the target vehicle detection device. This target vehicle detection device detected the target vehicle during a historical period, and the time when this target vehicle detection device detected the target vehicle is later than the time when any other vehicle detection device detected the target vehicle, that is, the target vehicle detection device is the vehicle detection device that detected the target vehicle last.

[0092] In step S501C, since the target vehicle detection device is the vehicle detection device that detected the target vehicle last, it can be determined that the detection area where the target vehicle may be currently located is the detection area where the target vehicle detection device is located. For example, for Figure 3 , Figure 4 or Figure 5 In the shown cases, if the target vehicle detection device is vehicle detection device A, vehicle detection device B, vehicle detection device C, or vehicle detection device D, then it can be determined that the detection area where the target vehicle may be currently located is the first detection area ABCD.

[0093] When the background server executes step S5, that is, according to the identification information of the target vehicle detection device, queries in the detection section data and the detection area data to track the position of the target vehicle, on the basis of executing step S501C, the following steps can also be executed:

[0094] S502C. Determine the cumulative duration when the target vehicle detection device detected the target vehicle;

[0095] S503C. Determine the interval duration from when the target vehicle detection device last detected the target vehicle to the current time;

[0096] S504C. Determine the likelihood of the target vehicle being in the detection area according to the cumulative duration and the interval duration.

[0097] In step S502C, referring to Figure 5 , the background server samples the position of the target vehicle at different times such as t1 and t2 through each vehicle detection device, so as to obtain the moving trajectory of the target vehicle, and as long as the vehicle detection devices (including vehicle detection device A, vehicle detection device B, vehicle detection device C, or vehicle detection device D) within the first detection area ABCD still detect the target vehicle, continuously calculate the cumulative duration until any vehicle detection device within the first detection area ABCD no longer detects the target vehicle, and stop calculating the cumulative duration. The significance of the cumulative duration is the continuous time when the target vehicle is determined to appear within the first detection area ABCD.

[0098] In step S503C, starting from the moment when no target vehicle has been detected by any vehicle detection device within the first detection area ABCD, calculate the elapsed time until step S504C is executed (i.e., the current moment).

[0099] In step S504C, set a function for calculating the likelihood. This function for calculating the likelihood is an increasing function of the cumulative time calculated in step S502C and a decreasing function of the elapsed time calculated in step S503C. Substitute the cumulative time and the elapsed time into the function for calculating the likelihood to calculate the likelihood. Therefore, the likelihood is positively correlated with the cumulative time and negatively correlated with the elapsed time.

[0100] The function for calculating the likelihood used in step S504C can be a function such as the ratio of the cumulative time to the elapsed time, or the difference between the cumulative time and the elapsed time.

[0101] The principle of steps S502C - S504C is as follows: The longer the cumulative time that the target vehicle detection device has detected the target vehicle, the greater the likelihood that the target vehicle remains in the detection area where the target vehicle detection device is located at the current moment (for example, if the target vehicle detection device is vehicle detection device A, then the detection area is the first detection area ABCD) (it may stay longer for reasons such as handling affairs, etc.). And the longer the time since no target vehicle has been detected by any vehicle detection device within the detection area (for example, the target vehicle has disappeared from the first detection area ABCD), the greater the likelihood that the target vehicle has left the detection area where the target vehicle detection device is located, that is, the smaller the likelihood that the target vehicle is still within the detection area where the target vehicle detection device is located. Therefore, the likelihood obtained by executing steps S502C - S504C is related to the probability that the target vehicle is still within the detection area where the target vehicle detection device is located.

[0102] The likelihood obtained by executing steps S502C - S504C is not necessarily the probability itself that the target vehicle is still within the detection area where the target vehicle detection device is located. Steps S502C - S504C can also be executed for other vehicle detection devices that have detected the target vehicle during a historical period and their corresponding detection areas, so as to obtain the likelihoods corresponding to other vehicle detection devices and their detection areas. Through the sorting of the likelihoods, a reference order of the likelihoods of the target vehicle being in which detection area currently can be obtained, which plays an objective reference role in the tracking work of the vehicle and helps to determine the position of the vehicle as soon as possible.

[0103] There is a situation where the vehicle is not on the road, and it is unknown when and where the vehicle will hit the road. At this time, the vehicle high-frequency driving model is used to obtain the sections and time periods where the target vehicle drives frequently, so as to select appropriate sections and time periods for deployment and wait for the rabbit by the tree stump.

[0104] The vehicle high-frequency driving model uses the detection data of the target vehicle in the recent 30 days as the data source, counts the corresponding data volume according to the device number, the detected direction, and the detected lane, and then arranges them in descending order from large to small. The top 10 are the positions where the vehicle frequently passes in this embodiment; then, the passing time is respectively counted for each frequently passing position, and statistics are made with 15 minutes as a time window. For example, there are 4 time windows between 9 o'clock and 10 o'clock, namely 9:00, 9:15, 9:30, and 9:45. The statistical results are arranged in descending order from large to small, and the top 3 are the high-frequency passing time windows of the vehicle at a certain frequently passing position.

[0105] The computer program for implementing the vehicle tracking method in this embodiment can be written, and this computer program is written into a computer device or a storage medium. When the computer program is read and run, the vehicle tracking method in this embodiment is executed, so as to achieve the same technical effect as the vehicle tracking method in the embodiment.

[0106] It should be noted that, unless otherwise specified, when a certain feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to another feature, or indirectly fixed or connected to another feature. In addition, the up, down, left, and right descriptions used in this disclosure are only relative to the mutual positional relationship of the components of this disclosure in the drawings. The singular forms "a", "the", and "said" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all the technical and scientific terms used in this embodiment have the same meaning as those commonly understood by those skilled in the art of this technology. The terms used in the description of this embodiment are only for describing specific embodiments, rather than for limiting the present invention. The term "and / or" used in this embodiment includes any combination of one or more of the related listed items.

[0107] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, without departing from the scope of this disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "such as", etc.) provided in this embodiment is only intended to better illustrate the embodiments of the present invention and will not impose a limitation on the scope of the present invention unless otherwise required.

[0108] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method can be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with the computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and figures described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program is capable of running on a programmed application-specific integrated circuit.

[0109] In addition, the operations of the processes described in this embodiment can be performed in any suitable order, unless this embodiment otherwise indicates or is otherwise clearly contradicted by the context. The processes described in this embodiment (or variations and / or combinations thereof) can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executed commonly on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions executable by one or more processors.

[0110] Further, the method can be implemented in any type of computing platform operatively connected to a suitable one, including but not limited to personal computers, minicomputers, mainframes, workstations, network or distributed computing environments, separate or integrated computer platforms, or communicating with charged particle tools or other imaging devices, etc. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into the computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it can be read by a programmable computer and can be used to configure and operate the computer to perform the processes described herein when the storage medium or device is read by the computer. Additionally, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. When such media includes instructions or programs that implement the above-described steps in conjunction with a microprocessor or other data processor, the invention as described in this embodiment includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0111] A computer program can be applied to input data to perform the functions described in this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the transformed data represents physical and tangible objects, including a specific visual depiction of the physical and tangible objects generated on the display.

[0112] As described above, these are only the preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. As long as the same means are used to achieve the technical effects of the present invention, any modifications, equivalent replacements, improvements, etc., made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, its technical solutions and / or implementation manners can have various different modifications and changes.

Claims

1. A method for tracking a vehicle, characterized in that, The vehicle tracking method described above includes: Obtaining detection section data; the detection section data records the identification information of multiple vehicle detection devices and the azimuth information of the detection ranges of each of the vehicle detection devices; Obtaining detection area data; the detection area data records the identification information of multiple combinations of vehicle detection devices; each combination of vehicle detection devices includes multiple vehicle detection devices, and the detection ranges of the vehicle detection devices in the same combination of vehicle detection devices are used as boundaries to enclose a detection area; Determining a target vehicle detection device; the target vehicle detection device is the vehicle detection device that detects the target vehicle; Obtaining the identification information of the target vehicle detection device; Querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle; The querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle, includes: When the target vehicle detection device detects the target vehicle in real time currently, querying in the detection area data according to the identification information of the target vehicle detection device to determine the detection area where the target vehicle is located; Predicting the position of the target vehicle according to the detection range and the detection area where the target vehicle is located; The predicting the position of the target vehicle according to the detection range and the detection area where the target vehicle is located, includes: Determining the detection area where the target vehicle is located as the first detection area; Obtaining the boundary direction of the first detection area; Starting from the current position of the target vehicle, determining the internal position of the first detection area pointed to by the boundary direction; Taking the internal position as the prediction result of the position of the target vehicle; Or Determining the detection area where the target vehicle is located as the first detection area; Obtaining the boundary direction of the first detection area; Determining the cumulative moving distance of the target vehicle within the first detection area; When the cumulative moving distance reaches a preset threshold, starting from the current position of the target vehicle, determining a second detection area pointed to by the boundary direction; Taking the second detection area as the prediction result of the position of the target vehicle.

2. The method for tracking a vehicle according to claim 1, characterized in that, The querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle, further includes: When the target vehicle detection device detects the target vehicle in real time currently, querying in the detection section data according to the identification information of the target vehicle detection device to determine the detection range where the target vehicle is located; Determining the current position of the target vehicle according to the detection range where the target vehicle is located.

3. The method for tracking a vehicle according to claim 1, characterized in that, The querying in the detection section data and the detection area data according to the identification information of the target vehicle detection device, so as to track the position of the target vehicle, includes: When the target vehicle detection device has detected the target vehicle in a historical period, query in the detection area data according to the identification information of the target vehicle detection device to determine the detection area where the target vehicle may currently be located.

4. The method for tracking a vehicle according to claim 3, characterized in that, The query in the detection section data and the detection area data according to the identification information of the target vehicle detection device to track the position of the target vehicle further includes: Determine the cumulative duration for which the target vehicle detection device has detected the target vehicle; Determine the interval duration from the last detection of the target vehicle by the target vehicle detection device to the current time; According to the cumulative duration and the interval duration, determine the likelihood of the target vehicle being in the detection area.

5. The method for tracking a vehicle according to claim 4, characterized in that, The likelihood is positively correlated with the cumulative duration and negatively correlated with the interval duration.

6. A computer device, characterized in that, It includes a memory and a processor. The memory is used to store at least one program, and the processor is used to load the at least one program to execute the vehicle tracking method according to any one of claims 1-5.

7. A storage medium storing a program executable by a processor, characterized in that, The program executable by the processor, when executed by the processor, is used to execute the vehicle tracking method according to any one of claims 1-5.

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