Track detection method and device

Abstract

The invention provides a trajectory detection method and device, and the method comprises the steps: determining a trajectory offset point of a target body based on the trajectory data of the target body; judging whether the determined trajectory offset point is an abnormal offset point or not; if yes, removing the abnormal offset point to obtain a target track; and track detection is carried out by using the target track and a predetermined track. According to the embodiment, the abnormal offset points in the trajectory data can be eliminated, so that the accuracy of trajectory detection is ensured.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a trajectory detection method and apparatus. Background Technology

[0002] In some scenarios, it is necessary to monitor the trajectory of a vehicle to ensure its accuracy. However, when monitoring the trajectory points of a vehicle, abnormal deviations may occur due to unstable signals or other reasons. If these abnormal deviation points are detected together with normal trajectory points, the accuracy of trajectory detection will be affected. Summary of the Invention

[0003] In view of this, the present disclosure provides at least one trajectory detection method, apparatus, electronic device, and storage medium, which can improve the accuracy of trajectory detection.

[0004] In a first aspect, embodiments of this disclosure provide a trajectory detection method, including:

[0005] The trajectory offset point of the target subject is determined based on the trajectory data of the target subject;

[0006] Determine whether the identified trajectory offset points are abnormal offset points; if so, remove the abnormal offset points to obtain the target trajectory.

[0007] Trajectory detection is performed using the target trajectory and the predetermined trajectory.

[0008] Optionally, determining the trajectory offset point of the target subject based on the trajectory data of the target subject includes:

[0009] For any trajectory point in the trajectory data of the target subject, determine the shortest distance between the trajectory point and each trajectory line segment associated with the predetermined trajectory; wherein, the trajectory line segment is the line connecting two adjacent coordinate points on the predetermined trajectory;

[0010] Based on each of the shortest distances, determine the offset distance of the trajectory point from the predetermined trajectory;

[0011] Determine whether the offset distance exceeds a preset threshold; if so, use the trajectory point as the trajectory offset point.

[0012] Optionally, determining the trajectory offset point of the target subject based on the trajectory data of the target subject includes:

[0013] For any trajectory point in the trajectory data of the target subject, a target line segment is obtained by filtering from the predetermined trajectory based on a pre-established index tree and a preset index range; wherein, the target line segment is the line connecting two adjacent coordinate points on the predetermined trajectory that satisfy the preset index range;

[0014] Determine the shortest distance between the trajectory point and each target line segment, and based on each shortest distance, determine the offset distance between the trajectory point and the predetermined trajectory;

[0015] Determine whether the offset distance exceeds a preset threshold; if so, use the trajectory point as the trajectory offset point.

[0016] Optionally, the shortest distance from the trajectory point to the corresponding line segment can be determined by the following steps:

[0017] Determine the projection points of the trajectory points onto the corresponding line segments;

[0018] If the projection point is located on the corresponding line segment, then the line connecting the trajectory point and the projection point is taken as the shortest distance;

[0019] If the projection point is not on the corresponding line segment, then the shortest connection between the trajectory point and the two endpoints of the corresponding line segment is taken as the shortest distance.

[0020] Optionally, determining whether the determined trajectory offset point is an abnormal offset point includes:

[0021] Determine whether the number of consecutive trajectory offset points is less than a preset threshold; if so, then treat the consecutive trajectory offset points as abnormal offset points.

[0022] Optionally, determining whether the determined trajectory offset point is an abnormal offset point includes:

[0023] The previous coordinate point adjacent to the trajectory offset point is used as the reference point;

[0024] Based on the coordinate data of the trajectory offset point and the coordinate data of the reference point, the kinematic data of the target body as it moves from the trajectory offset point to the reference point are calculated; the kinematic data includes at least one of steering angle and acceleration;

[0025] Determine whether the motion data exceeds a preset threshold. If so, the trajectory offset point is taken as an abnormal offset point.

[0026] Optionally, before determining the trajectory offset point of the target subject based on the trajectory data of the target subject, the method further includes:

[0027] For any trajectory point in the trajectory data of the target subject, determine whether the number of times the trajectory point appears in the abnormal location list is greater than a preset threshold.

[0028] If so, the trajectory points are removed from the trajectory data of the target entity;

[0029] After determining the abnormal offset point, the method further includes:

[0030] Add the abnormal offset point to the abnormal location list.

[0031] Secondly, embodiments of this disclosure provide a trajectory detection device, comprising:

[0032] The determination module is used to determine the trajectory offset point of the target subject based on the trajectory data of the target subject;

[0033] The judgment module is used to determine whether the determined trajectory offset point is an abnormal offset point; if so, the abnormal offset point is removed to obtain the target trajectory.

[0034] The detection module is used to perform trajectory detection using the target trajectory and the predetermined trajectory.

[0035] Thirdly, embodiments of this disclosure also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the first aspect above, or any optional implementation of the first aspect, are performed.

[0036] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the first aspect or any optional implementation thereof.

[0037] In any of the above aspects or any implementation thereof, the trajectory offset point of the target subject is determined based on the trajectory data of the target subject; it is determined whether the determined trajectory offset point is an abnormal offset point; if so, the abnormal offset point is removed to obtain the target trajectory; and trajectory detection is performed using the target trajectory and a predetermined trajectory. Therefore, after determining the trajectory offset point of the target subject, abnormal offset points can be removed from the trajectory offset points, thereby eliminating noise interference in the target trajectory used for trajectory detection and improving the accuracy of trajectory detection.

[0038] In a further embodiment of this disclosure, for any trajectory point in the trajectory data of the target subject, a target line segment is obtained by filtering from a predetermined trajectory based on a pre-established index tree and a preset index range; wherein, the target line segment is the line connecting two adjacent coordinate points on the predetermined trajectory that satisfy the preset index range; the shortest distance from the trajectory point to each target line segment is determined, and based on each shortest distance, the offset distance of the trajectory point from the predetermined trajectory is determined; it is determined whether the offset distance exceeds a preset threshold; if so, the trajectory point is taken as a trajectory offset point. Thus, the use of the index tree and index range reduces the computational burden when determining trajectory offset points, thereby improving the efficiency of trajectory detection.

[0039] In a further embodiment of this disclosure, the method involves determining whether the number of consecutive trajectory offset points is less than a preset threshold; if so, the consecutive trajectory offset points are designated as abnormal offset points; and the previous coordinate point adjacent to the trajectory offset point is designated as a reference point; based on the coordinate data of the trajectory offset points and the coordinate data of the reference point, the kinematic data of the target body moving from the trajectory offset point to the reference point is calculated; the kinematic data includes at least one of steering angle and acceleration; and determining whether the motion data exceeds a preset threshold, if so, the trajectory offset point is designated as an abnormal offset point. Since changes in trajectory points may occur due to signal instability or other reasons when recording the trajectory data of the target body, the above method can be provided to determine whether an offset trajectory point is an abnormal offset point. If so, it can be removed to ensure the accuracy of trajectory detection.

[0040] In a further embodiment of this disclosure, for any trajectory point in the trajectory data of the target subject, it is determined whether the number of times the trajectory point appears in the abnormal location list is greater than a preset threshold; if so, the trajectory point is removed from the trajectory data of the target subject; after determining the abnormal offset point, the method further includes adding the abnormal offset point to the abnormal location list. This allows for the direct removal of a trajectory point as an abnormal offset point when it repeatedly exhibits anomalies at a certain location, eliminating the waste of computational resources for its determination and thus improving the efficiency of trajectory detection.

[0041] The effects of the aforementioned trajectory detection device, electronic equipment, and storage medium are described in the description of the trajectory detection method above, and will not be repeated here. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0043] Figure 1 A flowchart of a trajectory detection method provided in an embodiment of this disclosure is shown;

[0044] Figure 2 A schematic diagram of a method for determining the shortest distance in the trajectory detection method provided in this disclosure is shown.

[0045] Figure 3 A schematic diagram of a trajectory offset in the trajectory detection method provided in this embodiment of the present disclosure is shown;

[0046] Figure 4 A schematic diagram of a trajectory detection device provided in an embodiment of this disclosure is shown;

[0047] Figure 5 An exemplary system architecture in which embodiments of this disclosure can be applied is shown;

[0048] Figure 6 A schematic diagram of the structure of a computer system used to implement the embodiments of the present disclosure is shown. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0050] It should be noted that the collection, use, storage, sharing and transfer of user personal information involved in the technical solution of the present invention all comply with the provisions of relevant laws and regulations, and require notification to users and obtaining their consent or authorization. When applicable, user personal information is subjected to de-identification and / or anonymization and / or encryption technical processing.

[0051] Research has found that in some scenarios, it is necessary to monitor the trajectory data of the driving entity to ensure the rationality of its trajectory. For example, in logistics scenarios, it is necessary to monitor the driving route of the loading vehicle to ensure that the loading vehicle can travel along the pre-planned route, thereby ensuring the timeliness of logistics transportation; or in online ride-hailing scenarios, it is necessary to monitor the driving route of the ride-hailing vehicle to ensure passenger safety, etc.

[0052] However, when monitoring the trajectory data of a vehicle, there may be sudden changes in trajectory points due to reasons such as unstable signals. Such abnormal offset points do not match the actual driving trajectory of the vehicle. If abnormal offset points are detected together with normal trajectory points, it will affect the accuracy of the final trajectory detection results.

[0053] Based on the above research, this disclosure provides a trajectory detection method. The method determines the trajectory offset point of the target subject based on its trajectory data; determines whether the determined trajectory offset point is an abnormal offset point; if so, the abnormal offset point is removed to obtain the target trajectory; and performs trajectory detection using the target trajectory and a predetermined trajectory. Thus, after determining the trajectory offset point of the target subject, abnormal offset points can be removed, ensuring that the target trajectory used for trajectory detection is free from noise interference, thereby improving the accuracy of trajectory detection. Furthermore, for any trajectory point in the target subject's trajectory data, a target line segment is selected from the predetermined trajectory based on a pre-established index tree and a preset index range; wherein the target line segment is the line connecting two adjacent coordinate points on the predetermined trajectory that satisfy the preset index range; the shortest distance from the trajectory point to each target line segment is determined; based on each shortest distance, the offset distance from the trajectory point to the predetermined trajectory is determined; it is determined whether the offset distance exceeds a preset threshold; if so, the trajectory point is used as the trajectory offset point. This reduces the computational burden of determining trajectory deviation points by utilizing an index tree and index range, thereby improving the efficiency of trajectory detection. Furthermore, by determining whether the number of consecutive trajectory deviation points is less than a preset threshold, if so, the consecutive trajectory deviation points are designated as abnormal deviation points; and the previous coordinate point adjacent to the trajectory deviation point is used as a reference point; based on the coordinate data of the trajectory deviation point and the coordinate data of the reference point, the kinematic data of the target entity moving from the trajectory deviation point to the reference point is calculated; the kinematic data includes at least one of steering angle and acceleration; it is determined whether the kinematic data exceeds a preset threshold, and if so, the trajectory deviation point is designated as an abnormal deviation point. Since sudden changes in trajectory points may occur due to signal instability or other reasons when recording the trajectory data of the target entity, the above method can be provided to determine whether a deviation trajectory point is an abnormal deviation point, and if so, it can be removed to ensure the accuracy of trajectory detection. Furthermore, for any trajectory point in the trajectory data of the target subject, it is determined whether the number of times the trajectory point appears in the abnormal location list exceeds a preset threshold; if so, the trajectory point is removed from the trajectory data of the target subject. After determining the abnormal offset point, the method further includes adding the abnormal offset point to the abnormal location list. This allows for the direct removal of a trajectory point as an abnormal offset point when it repeatedly exhibits anomalies, eliminating the waste of computational resources for its judgment and thus improving the efficiency of trajectory detection.

[0054] The above problems and solutions are the result of the inventor's practice and careful research. The discovery process of the above problems and the solutions proposed for the above problems should be considered as the inventor's contribution to this disclosure.

[0055] The technical solutions of this disclosure will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely to illustrate selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0056] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0057] To facilitate understanding of this embodiment, a trajectory detection method disclosed in this disclosure will first be described in detail. The execution entity of the trajectory detection method provided in this disclosure is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the trajectory detection method can be implemented by a processor calling computer-readable instructions stored in memory.

[0058] See Figure 1 The diagram shows a flowchart of a trajectory detection method provided in an embodiment of this disclosure. The method includes steps S101 to S103, wherein:

[0059] S101: Determine the trajectory offset point of the target subject based on the trajectory data of the target subject.

[0060] In this embodiment of the disclosure, the trajectory data of the target subject may include multiple trajectory points. The trajectory points in the trajectory data may be collected by a collection device embedded in the target subject according to a preset period, or they may be collected according to the distance traveled. This embodiment of the disclosure does not make specific limitations in this regard.

[0061] In this embodiment, trajectory offset points are those trajectory points in the actual trajectory data of the target entity that deviate from the predetermined trajectory. Due to actual road conditions and other issues, trajectory points in the actual trajectory data may not completely coincide with the predetermined trajectory. Trajectory points with low deviation can be considered normal trajectory points, while trajectory points with high deviation can be identified as trajectory offset points. The degree of deviation can be represented by indicators such as deviation distance, deviation speed, and deviation angle.

[0062] Specifically, when using offset distance to represent the degree of offset, determining the trajectory offset point of the target subject based on the trajectory data of the target subject includes: for any trajectory point in the trajectory data of the target subject, determining the shortest distance between the trajectory point and each trajectory line segment associated with the predetermined trajectory; wherein, the trajectory line segment is the line connecting two adjacent coordinate points on the predetermined trajectory; based on each of the shortest distances, determining the offset distance of the trajectory point from the predetermined trajectory; determining whether the offset distance exceeds a preset threshold; if so, then taking the trajectory point as the trajectory offset point.

[0063] In specific implementation, such as Figure 2 As shown, curve L is the predetermined trajectory. Let A be a trajectory point in the target subject's trajectory data, and B be two coordinate points on a predetermined trajectory L. Line segment AB is a trajectory line segment associated with the predetermined trajectory. This can be calculated... The shortest distance between the point and line segment AB determines the trajectory point. Is this a trajectory offset point? It should be noted that... Figure 2 Only the distance is shown. When determining whether a trajectory point is a trajectory offset point, the most recent trajectory segment can be used to calculate the trajectory point. The distance between each trajectory segment associated with the predetermined trajectory L is used to determine the shortest distance among them. The offset distance from the predetermined trajectory. After obtaining the offset distance, it can be determined whether the offset distance exceeds a preset threshold, that is, to determine the trajectory point. If the degree of offset exceeds the preset range, then the trajectory point can be used as the trajectory offset point.

[0064] In a preferred embodiment, determining the trajectory offset point of the target subject based on the trajectory data of the target subject includes: for any trajectory point in the trajectory data of the target subject, selecting target line segments from a predetermined trajectory based on a pre-established index tree and a preset index range; wherein, the target line segment is a line connecting two adjacent coordinate points on the predetermined trajectory that satisfy the preset index range; determining the shortest distance from the trajectory point to each target line segment, and determining the offset distance of the trajectory point from the predetermined trajectory based on each shortest distance; determining whether the offset distance exceeds a preset threshold; if so, then using the trajectory point as the trajectory offset point.

[0065] In practical implementation, if the method described above is followed, each trajectory point in the trajectory data needs to traverse all trajectory segments associated with the predetermined trajectory, resulting in high time complexity. However, for each trajectory point, the nearest line segment must be within a certain range. If no trajectory segment with a distance less than a preset threshold can be found beyond a certain range, the trajectory point can be directly identified as a trajectory offset point without calculating the distance between the trajectory point and each trajectory segment. Based on this, the trajectory segments associated with the predetermined trajectory can be stored in an index tree as spatial objects, and an index range can be set for each trajectory point. Then, for each trajectory point, all trajectory segments within the preset index range can be queried through the index tree. If no trajectory segment is found, the trajectory point can be directly used as a trajectory offset point without further calculation; if a trajectory segment is found, it can be used as the target segment, and only the shortest distance between the trajectory point and the corresponding target segment needs to be calculated, without calculating the shortest distance between the trajectory point and all trajectory segments, greatly saving computational resources and thus improving the efficiency of trajectory detection.

[0066] In practical implementation, when using the offset angle to represent the degree of offset, a reference point on a predetermined trajectory can be selected, and the trajectory point closest to the selected reference point can be found in the trajectory data of the target subject. This trajectory point can be the point closest to the reference point, or it can be the point that best matches within a certain range. Then, the offset angle can be calculated using the coordinates of the reference point and the corresponding trajectory point. This offset angle can represent the degree of offset of the target subject's trajectory point. Furthermore, a preset threshold can be set to determine whether the trajectory point is a trajectory offset point.

[0067] It should be noted that the above description only uses offset distance and offset angle to represent the degree of offset as an example. In practical applications, offset speed, offset acceleration, offset area, offset time and other indicators can also be used to represent the degree of offset of trajectory points. This disclosure does not specifically limit this and will not elaborate further.

[0068] In specific implementation, the shortest distance from the trajectory point to the corresponding line segment can be determined according to the following steps: determine the projection point from the trajectory point to the corresponding line segment; if the projection point is located on the corresponding line segment, then the line connecting the trajectory point and the projection point is taken as the shortest distance; if the projection point is not on the corresponding line segment, then the shortest line connecting the trajectory point and the two endpoints of the corresponding line segment is taken as the shortest distance.

[0069] like Figure 2 As shown, to calculate trajectory points and Taking the shortest distance to trajectory line segment AB as an example, we can first determine the trajectory points. and The projection point of the trajectory line segment AB and ,like Figure 2 As shown, projection point Located on line segment AB, then we can... and The line connecting them serves as The shortest distance to the trajectory line segment AB; projection point If it is not on the trajectory line segment AB, then it can be... Connect the line segment AB to endpoints A and B respectively, and select the shortest connection as the shortest path. The shortest distance to trajectory line segment AB. Alternatively, the shortest distance can be calculated using the coordinate data of the trajectory point and the linear equation of the trajectory line segment, or the dot product of the line connecting the trajectory point to the trajectory line segment and the normal vector of the trajectory line segment can be calculated, and then divided by the magnitude of the normal vector to obtain the shortest distance, etc.; this disclosure does not specifically limit this method.

[0070] S102: Determine whether the determined trajectory offset point is an abnormal offset point; if so, remove the abnormal offset point to obtain the target trajectory.

[0071] In this step, after determining the trajectory offset, it is possible to further determine whether these trajectory offset points are abnormal offset points. If so, the abnormal offset points can be removed from the trajectory data of the target subject to improve the accuracy of trajectory detection.

[0072] In this embodiment, trajectory point anomalies may occur due to various factors such as signal instability, equipment failure, and environmental interference. If abnormal offset points are detected together with normal trajectory points, the accuracy of trajectory detection will be reduced. To solve this problem, this embodiment removes abnormal offset points from the trajectory offset points after determining them, before performing trajectory detection, thereby improving the accuracy of trajectory detection.

[0073] Specifically, determining whether a determined trajectory offset point is an abnormal offset point includes: determining whether the number of consecutive trajectory offset points is less than a preset threshold; if so, then the consecutive trajectory offset points are regarded as abnormal offset points.

[0074] When trajectory points in the target subject's trajectory data deviate from the predetermined trajectory, there are often two reasons: one is noise, and the other is that the trajectory path itself is different, meaning that from a certain trajectory point, the actual driving trajectory begins to separate from the predetermined trajectory. For example... Figure 3 As shown, the two trajectories overlap in different segments, and the non-overlapping parts cannot be directly considered noise points (i.e., abnormal offset points) and removed. Therefore, it is necessary to determine whether the trajectory offset points are noise points (i.e., abnormal offset points). In practical implementation, abnormal offset points caused by factors such as signal instability are often abrupt. Therefore, when N consecutive trajectory offset points occur, it can be determined whether N is less than a preset threshold. If it is less than a preset threshold, these trajectory points can be considered as abnormal offset points.

[0075] In another possible implementation, determining whether the determined trajectory deviation point is an abnormal deviation point includes: taking the previous coordinate point adjacent to the trajectory deviation point as a reference point; calculating the kinematic data of the target body moving from the trajectory deviation point to the reference point based on the coordinate data of the trajectory deviation point and the coordinate data of the reference point; the kinematic data includes at least one of steering angle and acceleration; determining whether the motion data exceeds a preset threshold, and if so, then designating the trajectory deviation point as an abnormal deviation point.

[0076] Since the target vehicle is subject to kinematic constraints during its movement, such as maximum steering angle, maximum speed, and maximum acceleration, the abnormality of trajectory deviation points can be determined by analyzing the relevant kinematic parameters of the target vehicle. In practice, the previous coordinate point adjacent to the trajectory deviation point can be selected as a reference point. Based on the coordinate data of these two points, the kinematic parameters of the target vehicle traveling from the reference point to the trajectory deviation point are calculated. These kinematic parameters are then compared with the kinematic constraint data corresponding to the target vehicle itself. If the calculated parameters exceed the tolerance range of the target vehicle, the trajectory deviation point is considered noise and can be removed as an abnormal deviation point. Alternatively, the next adjacent coordinate point can be selected as a reference point to determine if the trajectory deviation point is abnormal; or both the previous and next adjacent coordinate points can be selected simultaneously to determine if the trajectory deviation point is abnormal.

[0077] In this embodiment of the disclosure, after an abnormal offset point is determined, it can be added to an abnormal location list, and the coordinate data and other information of the abnormal offset point can be recorded in the abnormal location list. When the number of times the same abnormal offset point is recorded exceeds a preset threshold, the location information corresponding to the point can be regarded as an abnormal location, and it can be removed from the trajectory data of the target subject before the next trajectory offset point is determined, thereby improving the efficiency of determining abnormal offset points and thus improving the efficiency of trajectory detection.

[0078] In practice, when an anomaly occurs at a trajectory point at the same location, the corresponding coordinate data may have slight differences. To improve the accuracy and usability of the recording, the coordinate data of the abnormal offset point can be recorded in the form of a coordinate range.

[0079] S103: Perform trajectory detection using the target trajectory and the predetermined trajectory.

[0080] In this step, trajectory detection can be performed by comparing the target trajectory after removing abnormal offset points with the predetermined trajectory. Since noise in the trajectory data has been removed from the target trajectory, the accuracy of trajectory detection is improved.

[0081] According to a second aspect of the embodiments of this disclosure, such as Figure 4 As shown, a trajectory detection device 400 is provided, comprising:

[0082] The determining module 401 is used to determine the trajectory offset point of the target subject based on the trajectory data of the target subject;

[0083] The judgment module 402 is used to determine whether the determined trajectory offset point is an abnormal offset point; if so, the abnormal offset point is removed to obtain the target trajectory.

[0084] The detection module 403 is used to perform trajectory detection using the target trajectory and the predetermined trajectory.

[0085] Optionally, the determination module 401 is specifically used for:

[0086] For any trajectory point in the trajectory data of the target subject, determine the shortest distance between the trajectory point and each trajectory line segment associated with the predetermined trajectory; wherein, the trajectory line segment is the line connecting two adjacent coordinate points on the predetermined trajectory;

[0087] Based on each of the shortest distances, determine the offset distance of the trajectory point from the predetermined trajectory;

[0088] Determine whether the offset distance exceeds a preset threshold; if so, use the trajectory point as the trajectory offset point.

[0089] Optionally, the determination module 401 is specifically used for:

[0090] For any trajectory point in the trajectory data of the target subject, a target line segment is obtained by filtering from the predetermined trajectory based on a pre-established index tree and a preset index range; wherein, the target line segment is the line connecting two adjacent coordinate points on the predetermined trajectory that satisfy the preset index range;

[0091] Determine the shortest distance between the trajectory point and each target line segment, and based on each shortest distance, determine the offset distance between the trajectory point and the predetermined trajectory;

[0092] Determine whether the offset distance exceeds a preset threshold; if so, use the trajectory point as the trajectory offset point.

[0093] Optionally, the determination module 401 is specifically used for:

[0094] Determine the projection points of the trajectory points onto the corresponding line segments;

[0095] If the projection point is located on the corresponding line segment, then the line connecting the trajectory point and the projection point is taken as the shortest distance;

[0096] If the projection point is not on the corresponding line segment, then the shortest connection between the trajectory point and the two endpoints of the corresponding line segment is taken as the shortest distance.

[0097] Optionally, the judgment module 402 is specifically used for:

[0098] Determine whether the number of consecutive trajectory offset points is less than a preset threshold; if so, then treat the consecutive trajectory offset points as abnormal offset points.

[0099] Optionally, the judgment module 402 is specifically used for:

[0100] The previous coordinate point adjacent to the trajectory offset point is used as the reference point;

[0101] Based on the coordinate data of the trajectory offset point and the coordinate data of the reference point, the kinematic data of the target body as it moves from the trajectory offset point to the reference point are calculated; the kinematic data includes at least one of steering angle and acceleration;

[0102] Determine whether the motion data exceeds a preset threshold. If so, the trajectory offset point is taken as an abnormal offset point.

[0103] Optionally, the determination module 402 is also used for:

[0104] For any trajectory point in the trajectory data of the target subject, determine whether the number of times the trajectory point appears in the abnormal location list is greater than a preset threshold.

[0105] If so, the trajectory points are removed from the trajectory data of the target entity;

[0106] After determining the abnormal offset point, the method further includes:

[0107] Add the abnormal offset point to the abnormal location list.

[0108] According to a third aspect of the present disclosure, an electronic device for trajectory detection is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the present invention.

[0109] According to a fourth aspect of the present disclosure, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the present invention.

[0110] Figure 5 An exemplary system architecture 500 is shown that can be applied to the trajectory detection method or trajectory detection device implemented in this disclosure.

[0111] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, and 503, a network 504, and a server 505. Network 504 serves as the medium for providing communication links between terminal devices 501, 502, and 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0112] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, and 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0113] Terminal devices 501, 502, and 503 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0114] Server 505 can be a server that provides various services, such as a backend management server (for example only) that supports shopping websites browsed by users using terminal devices 501, 502, and 503. The backend management server can process the received trajectory detection requests and send the processing results (for example only) back to the terminal devices.

[0115] It should be noted that the trajectory detection method provided in this embodiment of the invention is generally executed by server 505, and correspondingly, the trajectory detection device is generally installed in server 505. The trajectory detection method provided in this embodiment of the invention can also be executed by terminal devices 501, 502, and 503, and correspondingly, the trajectory detection device can be installed in terminal devices 501, 502, and 503.

[0116] It should be understood that Figure 5 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0117] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing a terminal device of the present invention. Figure 6 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0118] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0119] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0120] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.

[0121] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0123] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor. For example, a processor may include a determining module, a judging module, and a detecting module. The names of these modules do not necessarily limit the module itself; for example, the determining module may also be described as "a module for determining the trajectory offset point of the target subject based on the trajectory data of the target subject."

[0124] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the device, the device implements the following method:

[0125] The trajectory offset point of the target subject is determined based on the trajectory data of the target subject;

[0126] Determine whether the identified trajectory offset points are abnormal offset points; if so, remove the abnormal offset points to obtain the target trajectory.

[0127] Trajectory detection is performed using the target trajectory and the predetermined trajectory.

[0128] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A trajectory detection method, characterized in that, include: The trajectory offset point of the target subject is determined based on the trajectory data of the target subject; Determine whether the identified trajectory offset points are abnormal offset points; If so, the abnormal offset points are removed to obtain the target trajectory; Trajectory detection is performed using the target trajectory and the predetermined trajectory.

2. The method according to claim 1, characterized in that, Determining the trajectory offset point of the target subject based on its trajectory data includes: For any trajectory point in the trajectory data of the target subject, determine the shortest distance between the trajectory point and each trajectory line segment associated with the predetermined trajectory; wherein, the trajectory line segment is the line connecting two adjacent coordinate points on the predetermined trajectory; Based on each of the shortest distances, determine the offset distance of the trajectory point from the predetermined trajectory; Determine whether the offset distance exceeds a preset threshold; if so, use the trajectory point as the trajectory offset point.

3. The method according to claim 1, characterized in that, Determining the trajectory offset point of the target subject based on its trajectory data includes: For any trajectory point in the trajectory data of the target subject, a target line segment is obtained by filtering from the predetermined trajectory based on a pre-established index tree and a preset index range; wherein, the target line segment is the line connecting two adjacent coordinate points on the predetermined trajectory that satisfy the preset index range; Determine the shortest distance between the trajectory point and each target line segment, and based on each shortest distance, determine the offset distance between the trajectory point and the predetermined trajectory; Determine whether the offset distance exceeds a preset threshold; if so, use the trajectory point as the trajectory offset point.

4. The method according to claim 2 or 3, characterized in that, Determine the shortest distance from the trajectory point to the corresponding line segment using the following steps: Determine the projection points of the trajectory points onto the corresponding line segments; If the projection point is located on the corresponding line segment, then the line connecting the trajectory point and the projection point is taken as the shortest distance; If the projection point is not on the corresponding line segment, then the shortest connection between the trajectory point and the two endpoints of the corresponding line segment is taken as the shortest distance.

5. The method according to claim 1, characterized in that, Determining whether the identified trajectory offset points are abnormal offset points includes: Determine whether the number of consecutive trajectory offset points is less than a preset threshold; if so, then treat the consecutive trajectory offset points as abnormal offset points.

6. The method according to claim 1, characterized in that, Determining whether the identified trajectory offset points are abnormal offset points includes: The previous coordinate point adjacent to the trajectory offset point is used as the reference point; Based on the coordinate data of the trajectory offset point and the coordinate data of the reference point, the kinematic data of the target body as it moves from the trajectory offset point to the reference point are calculated; the kinematic data includes at least one of steering angle and acceleration; Determine whether the motion data exceeds a preset threshold. If so, the trajectory offset point is taken as an abnormal offset point.

7. The method according to claim 1, characterized in that, Before determining the trajectory offset point of the target subject based on the trajectory data of the target subject, the method further includes: For any trajectory point in the trajectory data of the target subject, determine whether the number of times the trajectory point appears in the abnormal location list is greater than a preset threshold. If so, the trajectory points are removed from the trajectory data of the target entity; After determining the abnormal offset point, the method further includes: Add the abnormal offset point to the abnormal location list.

8. A trajectory detection device, characterized in that, include: The determination module is used to determine the trajectory offset point of the target subject based on the trajectory data of the target subject; The judgment module is used to determine whether the determined trajectory offset point is an abnormal offset point; If so, the abnormal offset points are removed to obtain the target trajectory; The detection module is used to perform trajectory detection using the target trajectory and the predetermined trajectory.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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