A target object trajectory moving mode recognition method, device, equipment and medium

Abstract

The application discloses a target object trajectory movement mode recognition method and device, equipment and medium, relates to the technical field of data analysis, and comprises the following steps: acquiring trajectory data of a target object, performing linear interpolation and low-pass filtering smoothing processing on the trajectory data, and obtaining a trajectory sequence; calculating an instantaneous displacement direction and a discrete curvature based on the trajectory sequence, determining trajectory sequence direction quadrant information and trajectory curvature information by using the instantaneous displacement direction and the discrete curvature; mapping and compressing the trajectory sequence direction quadrant information and the trajectory curvature information to generate a character sequence; constructing sequence templates of different trajectory types, comparing the sequence templates of different trajectory types with the character sequence; the sequence template is a template representing the trajectory movement mode of the target object; and identifying the trajectory movement mode of the target object based on the comparison result, which can identify various target object trajectory movement modes with movement and rotation invariance, and improve the efficiency and universality of trajectory movement mode recognition.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method, apparatus, device, and medium for recognizing the trajectory movement pattern of a target object. Background Technology

[0002] Trajectory data contains the historical movement states of target objects, such as aircraft, animals, ships, and vehicles. Mining movement patterns from trajectory data can reveal individual object behaviors and characterize key events. Existing techniques mostly focus on group movement pattern mining and trend analysis, such as cluster classification analysis, or on extracting specific behavioral patterns of individual trajectories that affect downstream tasks, such as stationary behavior. The above studies have not paid attention to the identification of complex movement patterns of individual trajectories, such as S-shaped, Z-shaped turning, C-shaped, and O-shaped circling movements. These movement patterns are external representations of key object behaviors and direct evidence of high-value events of the object, such as aircraft takeoff and landing and maneuvering, ship collision avoidance, and vehicle yaw. Moreover, these movement patterns usually have multiple changes in movement angle. Therefore, there is an urgent need to develop an efficient, robust, and universal method to identify these multiple target object trajectory movement patterns that are invariant to movement and rotation.

[0003] As can be seen from the above, how to identify various trajectory movement patterns of target objects with invariance to translation and rotation, and improve the efficiency and versatility of trajectory movement pattern recognition, is a problem to be solved in this field. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for recognizing the trajectory movement pattern of a target object, capable of recognizing various trajectory movement patterns of target objects with invariance to movement and rotation, thereby improving the efficiency and versatility of trajectory movement pattern recognition. The specific solution is as follows:

[0005] In a first aspect, this application discloses a method for recognizing the trajectory movement pattern of a target object, including:

[0006] The trajectory data of the target object is acquired, and the trajectory data is subjected to linear interpolation and low-pass filtering smoothing to obtain a trajectory sequence.

[0007] The instantaneous displacement direction and discrete curvature are calculated based on the trajectory sequence, and the trajectory sequence direction quadrant information and trajectory curvature information are determined using the instantaneous displacement direction and the discrete curvature;

[0008] The trajectory sequence direction quadrant information and the trajectory curvature information are mapped and compressed to generate a character sequence;

[0009] Construct sequence templates for different trajectory types, compare the sequence templates for different trajectory types with the character sequence, and obtain the comparison results; the sequence template is a template representing the trajectory movement pattern of the target object;

[0010] The trajectory movement pattern of the target object is identified based on the comparison results.

[0011] Optionally, linear interpolation processing is performed on the trajectory data, including:

[0012] The distance between two adjacent continuous points in latitude and longitude coordinates is obtained by using the semi-versus formula to calculate the trajectory data.

[0013] Linear interpolation is performed on the distance based on a preset first step length to obtain a time-interval trajectory sequence.

[0014] Optionally, the trajectory data is subjected to low-pass filtering and smoothing to obtain a trajectory sequence, including:

[0015] The trajectory sequence is obtained by using a Schwarzky-Gore filter and performing low-pass filtering smoothing on the time-equal interval trajectory sequence based on a preset sliding window and a preset second step size.

[0016] Optionally, calculating the instantaneous displacement direction based on the trajectory sequence includes:

[0017] The instantaneous displacement direction is obtained by calculating the discrete sampled trajectory points in the trajectory sequence using the first-order difference approximation derivative;

[0018] The formula for calculating the direction of the instantaneous displacement is:

[0019] ;

[0020] ;

[0021] ;

[0022] in, The instantaneous displacement direction These are the coordinates of discrete sampled trajectory points in the trajectory sequence. To and The next adjacent discrete sampling trajectory point, It is the velocity vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the instantaneous direction of movement.

[0023] Optionally, calculating the discrete curvature based on the trajectory sequence includes:

[0024] The discrete curvature is obtained by calculating the discrete sampled trajectory points in the trajectory sequence using the second-order difference approximation derivative;

[0025] The formula for calculating the discrete curvature is:

[0026] ;

[0027] ;

[0028] ;

[0029] in, For discrete curvature, The velocity vector of the discrete sampled trajectory points in the latitude and longitude directions represents the instantaneous direction of movement. To and The velocity vector of the next adjacent discrete sampling trajectory point in the latitude and longitude direction. The acceleration vector of the discrete sampled trajectory points in the latitude and longitude directions represents the degree and direction of curvature.

[0030] Optionally, constructing sequence templates for different trajectory types includes:

[0031] Construct initial Z-type sequence templates, initial O-type sequence templates, and initial C-type sequence templates;

[0032] The initial Z-shaped sequence template, the initial O-shaped sequence template, and the initial C-shaped sequence template are varied in different directions to construct sequence templates of different trajectory types; the trajectory types include Z-shaped, O-shaped, and C-shaped.

[0033] Optionally, the step of comparing the sequence templates of different trajectory types with the character sequence to obtain a comparison result; and identifying the trajectory movement pattern of the target object based on the comparison result, includes:

[0034] Set the time range according to business needs;

[0035] Determine whether a target character sequence corresponding to the character sequence exists in the sequence templates of different trajectory types within the time range;

[0036] If, within the time range, there exists a target character sequence corresponding to the character sequence in the sequence templates of different trajectory types, then the trajectory movement pattern of the target object is identified based on the target character sequence.

[0037] Secondly, this application discloses a target object trajectory movement pattern recognition device, comprising:

[0038] The data acquisition and processing module is used to acquire trajectory data of the target object, perform linear interpolation and low-pass filtering smoothing on the trajectory data, and obtain a trajectory sequence.

[0039] The information determination module is used to calculate the instantaneous displacement direction and discrete curvature based on the trajectory sequence, and to determine the trajectory sequence direction quadrant information and trajectory curvature information using the instantaneous displacement direction and the discrete curvature;

[0040] The mapping and compression module is used to map and compress the direction quadrant information and the trajectory curvature information of the trajectory sequence to generate a character sequence;

[0041] The comparison module is used to construct sequence templates for different trajectory types, compare the sequence templates for different trajectory types with the character sequence, and obtain the comparison results; the sequence template is a template that represents the trajectory movement pattern of the target object;

[0042] The identification module is used to identify the trajectory movement pattern of the target object based on the comparison results.

[0043] Thirdly, this application discloses an electronic device, including:

[0044] Memory, used to store computer programs;

[0045] A processor is used to execute the computer program to implement the aforementioned target object trajectory movement pattern recognition method.

[0046] Fourthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned target object trajectory movement pattern recognition method.

[0047] As can be seen, this application provides a method for recognizing the trajectory movement pattern of a target object, including acquiring trajectory data of the target object, performing linear interpolation and low-pass filtering smoothing on the trajectory data to obtain a trajectory sequence; calculating the instantaneous displacement direction and discrete curvature based on the trajectory sequence, and using the instantaneous displacement direction and discrete curvature to determine the trajectory sequence direction quadrant information and trajectory curvature information; mapping and compressing the trajectory sequence direction quadrant information and trajectory curvature information to generate a character sequence; constructing sequence templates for different trajectory types, comparing the sequence templates of different trajectory types with the character sequence to obtain a comparison result; the sequence template is a template representing the trajectory movement pattern of the target object; and recognizing the trajectory movement pattern of the target object based on the comparison result. This application performs linear interpolation and low-pass filtering smoothing on the trajectory data of the target object to obtain a trajectory sequence. This avoids interference caused by uneven trajectory sampling and local drift jitter. While preserving the true motion trend of the trajectory, it suppresses high-frequency noise and eliminates the influence of trajectory sampling drift on subsequent direction calculation and curvature feature extraction. Based on the trajectory sequence, it calculates the instantaneous displacement direction and discrete curvature, and uses the instantaneous displacement direction and discrete curvature to determine the direction quadrant information and trajectory curvature information of the trajectory sequence. It accurately and finely segments and characterizes the local movement state of the trajectory. It maps and compresses the direction quadrant information and trajectory curvature information of the trajectory sequence to generate a character sequence, improving the accuracy and robustness of trajectory movement pattern recognition. It compares the sequence templates of different trajectory types with the character sequence to obtain the comparison results. Based on the comparison results, it identifies the trajectory movement pattern of the target object, and can identify a variety of target object trajectory movement patterns with translation and rotation invariance, improving the efficiency and versatility of trajectory movement pattern recognition. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0049] Figure 1 This is a flowchart of a target object trajectory movement pattern recognition method disclosed in this application;

[0050] Figure 2 This is an example diagram of the directional quadrant calculation results disclosed in this application;

[0051] Figure 3 This application discloses a visualization of the trajectory curvature.

[0052] Figure 4This is an example diagram of curvature intensity and direction disclosed in this application;

[0053] Figure 5 Here are the Z-type moving structure diagrams disclosed in this application, wherein (a) is a simplified first Z-type moving structure diagram and (b) is a simplified second Z-type moving structure diagram;

[0054] Figure 6 This is a schematic diagram of the structure of a target object trajectory movement pattern recognition device disclosed in this application;

[0055] Figure 7 This application provides a structural diagram of an electronic device. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Trajectory data contains the historical movement states of target objects, such as aircraft, animals, ships, and vehicles. Mining movement patterns in trajectory data can reveal individual object behaviors and characterize key events. Existing technologies mostly focus on group movement pattern mining and trend analysis, such as cluster classification analysis, or on extracting specific behavioral patterns of individual trajectories that affect downstream tasks, such as dwelling behavior. These studies have not addressed the identification of complex individual trajectory movement patterns, such as S-shaped, Z-shaped turning, C-shaped, and O-shaped circling movements. These movement patterns are external representations of key object behaviors and direct evidence of high-value events, such as aircraft takeoffs and landings and maneuvers, ship collision avoidance, and vehicle yaw. Moreover, these movement patterns often involve multiple changes in movement angles. Therefore, there is an urgent need to develop an efficient, robust, and universal method to identify these multiple target object trajectory movement patterns that are invariant to movement and rotation. As can be seen, how to identify multiple target object trajectory movement patterns with invariant movement and rotation, and improve the efficiency and universality of trajectory movement pattern recognition, is a problem that needs to be solved in this field.

[0058] See Figure 1 As shown, this embodiment of the invention discloses a method for recognizing the trajectory movement pattern of a target object, which may specifically include:

[0059] Step S11: Obtain the trajectory data of the target object, perform linear interpolation and low-pass filtering smoothing on the trajectory data to obtain the trajectory sequence.

[0060] In this embodiment, trajectory data of the target object is acquired, and the trajectory data is calculated using the semi-sine formula to obtain the distance between two adjacent continuous points in latitude and longitude coordinates. The distance is linearly interpolated based on a preset first step length to obtain a time-equal interval trajectory sequence. The time-equal interval trajectory sequence is smoothed by low-pass filtering using a Schwarzky-Gore filter based on a preset sliding window and a preset second step length to obtain the trajectory sequence.

[0061] In this embodiment, the target objects include, but are not limited to, aircraft, animals, ships, and vehicles. The trajectory data is preprocessed, including linear interpolation and low-pass filtering for smoothing, to overcome interference from uneven trajectory sampling and local drift jitter, providing stable input for subsequent feature extraction. The specific process is as follows:

[0062] (1) Use the Haversine formula to calculate two adjacent continuous points in the trajectory T under latitude and longitude coordinates. and The distance between them is linearly interpolated based on a preset first step length to obtain a time-interval trajectory sequence. ;

[0063] (2) with As input, a Savitzky-Golay filter is used, with 5 consecutive sampling points as a preset sliding window. A quadratic polynomial minimum fit with a preset second step size of 1 is applied. Low-pass filtering is performed to smooth the trajectory, thus obtaining the trajectory sequence. This smoothing strategy can suppress high-frequency noise and eliminate the impact of trajectory sampling drift on subsequent direction calculation and curvature feature extraction while preserving the true motion trend of the trajectory.

[0064] Step S12: Calculate the instantaneous displacement direction and discrete curvature based on the trajectory sequence, and use the instantaneous displacement direction and discrete curvature to determine the trajectory sequence direction quadrant information and trajectory curvature information.

[0065] In this embodiment, the first-order difference approximation derivative is used to calculate the discrete sampled trajectory points in the trajectory sequence to obtain the instantaneous displacement direction;

[0066] The formula for calculating the direction of the instantaneous displacement is:

[0067] ;

[0068] ;

[0069] ;

[0070] in, The instantaneous displacement direction These are the coordinates of discrete sampled trajectory points in the trajectory sequence. To and The next adjacent discrete sampling trajectory point, The velocity vector of the discrete sampled trajectory points in the latitude and longitude directions represents the instantaneous direction of movement;

[0071] The discrete curvature is obtained by calculating the discrete sampled trajectory points in the trajectory sequence using the second-order difference approximation derivative;

[0072] The formula for calculating the discrete curvature is:

[0073] ;

[0074] ;

[0075] ;

[0076] in, For discrete curvature, Let be the velocity vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the instantaneous direction of movement. To and The velocity vector of the next adjacent discrete sampling trajectory point in the latitude and longitude direction. Let be the acceleration vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the degree and direction of curvature. The denominator is slightly simplified without affecting the direction and relative intensity.

[0077] Step S13: Map and compress the trajectory sequence direction quadrant information and the trajectory curvature information to generate a character sequence.

[0078] This application introduces a trajectory direction quadrant and discrete curvature calculation method based on discrete differential, which efficiently and accurately segments and characterizes the local movement state of the trajectory, and accordingly performs quadrant mapping and compression on the trajectory to form a character sequence with movement semantic information. The specific process is as follows:

[0079] (1) Using trajectory sequences For the input, for discrete sampling trajectory points The instantaneous displacement direction is calculated using the first-order difference approximation derivative. Then, with the positive X-axis as 0°, the direction angle is divided into four quadrants in 90° intervals: 0 [0, 90] (Northeast), 1 [90, 180] (Northwest), 2 [180, 270] (Southwest), and 3 [270, 360] (Southeast), to determine the direction quadrant information of the trajectory sequence. Taking a local trajectory as an example, the direction quadrant calculation results are as follows: Figure 2As shown, based on the direction quadrant of the trajectory, the points where different quadrants intersect are marked as high curvature inflection points PT (Peak Turn).

[0080] (2) with For the input, for discrete sampling trajectory points The discrete curvature at this point is calculated using the second-order difference approximation derivative. And extract the inflection point information to obtain the trajectory curvature information.

[0081] Visualization of trajectory curvature, such as Figure 3 As shown in the curvature visualization, the lines represent trajectories, and the arrows indicate the direction and relative magnitude of the curvature vector. The arrows are perpendicular to the tangent of the trajectory and point towards the center of curvature, with their length proportional to the curvature value. It can be seen that the curvature vector of the curve is greatest at the crests and troughs, and smallest near the straight line segments, approaching 0.

[0082] Based on the calculated curvature The threshold is set adaptively by percentage, and the trajectory points with curvature greater than the threshold are used as turning inflection points, consistent with step (1), and marked as PT (not marked repeatedly).

[0083] Furthermore, the molecular cross product is a scalar, representing the direction of rotation of the acceleration relative to the velocity. The curvature intensity and direction are as follows... Figure 4 As shown, when The curvature vector points upwards (i.e., rotates counterclockwise), when The curvature vector points downwards (i.e., the turning direction is clockwise). Therefore, the symbol extraction formula for obtaining the trajectory turning direction information D is as follows:

[0084] ;

[0085] By using the second-order difference approximation derivative to calculate the curvature information of the trajectory, all information about the "rapid" and "slow" and "clockwise" turning of the point can be preserved, making the description of continuously sampled moving trajectories more robust and accurate.

[0086] (3) Map and compress the trajectory sequence's direction quadrant information and trajectory curvature information to form a character sequence with motion semantic information, clearly representing trajectory inflection points (turning forward and reverse information) and direction quadrant changes. For example, Figure 2 The local trajectory sequence in the image is mapped and compressed into: .

[0087] Step S14: Construct sequence templates for different trajectory types, compare the sequence templates for different trajectory types with the character sequence, and obtain the comparison results; the sequence template is a template that represents the trajectory movement pattern of the target object.

[0088] Step S15: Identify the trajectory movement pattern of the target object based on the comparison results.

[0089] In this embodiment, initial Z-shaped sequence templates, initial O-shaped sequence templates, and initial C-shaped sequence templates are constructed. These initial Z-shaped, O-shaped, and C-shaped sequence templates are varied in different directions to construct sequence templates of different trajectory types. A time range is set according to business requirements. It is determined whether a target character sequence corresponding to a character sequence exists in the sequence templates of different trajectory types within the time range. If a target character sequence corresponding to a character sequence exists in the sequence templates of different trajectory types within the time range, the trajectory movement pattern of the target object is identified based on the target character sequence. The trajectory types include Z-shaped, O-shaped, and C-shaped.

[0090] This application constructs Z-shaped, O-shaped, and C-shaped sequence templates based on the geometric curvature characteristics of the trajectory and the quadrant variation rules. The sequence templates of different trajectory types are compared with character sequences to achieve accurate identification of the trajectory movement pattern of the target object. The specific process is as follows:

[0091] (1) Constructing a Z-shaped sequence template. Observations show that the Z-shaped movement pattern first turns clockwise and then counterclockwise, typically containing two inflection points with opposite curvature directions. Considering that the trajectory movement pattern is translationally and rotationally indeformable, the trajectory that may exhibit Z-shaped movement is rotated to a specific angle, thus enabling the construction of a universal template to adapt to Z-shaped pattern matching at different angles. The simplified first Z-shaped movement structure is as follows: Figure 5 As shown in (a), the simplified second Z-shaped movement structure is as follows: Figure 5 As shown in (b). First, based on the forward and reverse directional changes of the two consecutive inflection points of the trajectory, an initial Z-shaped sequence template is constructed. Then, to simplify the calculation details without affecting the calculation results, the inflection points are... point to The direction is defined as the main direction of the Z-movement pattern and rotated to the horizontal direction of the X-axis; finally, quadrant mapping and compression are performed. Based on this, the Z-movement pattern can be organized into the following sequence template:

[0092] {2,+,*,-,2};

[0093] {1,-,*,+,2}.

[0094] Where * indicates an inflection point and The sequence quadrants between them are not required in template matching.

[0095] (2) Sequence templates for C-type and O-type. Based on the state of the character sequence, both C-type and O-type movement state sequences exhibit continuous increasing or decreasing quadrant changes. The difference lies in that O-type is a complete circular pattern, such as a sequence structure of {0,1,2,3}; while C-type has a sequence structure of {1,2,3,*}, where * indicates that it can be in any direction. Due to the clockwise / counterclockwise difference and the different starting quadrants, O-type and C-type may be accompanied by multiple sequence pattern expressions. Therefore, the relative quadrant changes of the characters in the latter quadrant compared to the characters in the former quadrant are calculated to establish a more general sequence template. For example, the {0,1,2,3} sequence structure can be converted to {*, 1,1,1}. Based on this, the O-type and C-type movement patterns can be organized into the following sequence templates:

[0096] Type O: {*,1,1,1}; {*,-1,-1,-1};

[0097] Type C: {*,1,1,*}; {*,-1,-1,*}.

[0098] (3) Within the time range, match sequence templates of different trajectory types with character sequences to achieve accurate recognition of this type of movement pattern.

[0099] This invention proposes a trajectory movement pattern recognition method for target objects based on geometric curvature features and directional quadrant change laws. It extracts explicit geometric curvature features and directional change laws of the trajectory, providing an efficient, robust, and universal method for recognizing various trajectory movement patterns with invariance to movement and rotation.

[0100] In this embodiment, trajectory data of the target object is acquired, and the trajectory data is subjected to linear interpolation and low-pass filtering smoothing to obtain a trajectory sequence. Based on the trajectory sequence, the instantaneous displacement direction and discrete curvature are calculated, and the instantaneous displacement direction and discrete curvature are used to determine the trajectory sequence's direction quadrant information and trajectory curvature information. The trajectory sequence's direction quadrant information and trajectory curvature information are mapped and compressed to generate a character sequence. Sequence templates for different trajectory types are constructed, and the sequence templates for different trajectory types are compared with the character sequence to obtain comparison results. The sequence template is a template representing the trajectory movement pattern of the target object. Based on the comparison results, the trajectory movement pattern of the target object is identified. This application performs linear interpolation and low-pass filtering smoothing on the trajectory data of the target object to obtain a trajectory sequence. This avoids interference caused by uneven trajectory sampling and local drift jitter. While preserving the true motion trend of the trajectory, it suppresses high-frequency noise and eliminates the influence of trajectory sampling drift on subsequent direction calculation and curvature feature extraction. Based on the trajectory sequence, it calculates the instantaneous displacement direction and discrete curvature, and uses the instantaneous displacement direction and discrete curvature to determine the direction quadrant information and trajectory curvature information of the trajectory sequence. It accurately and finely segments and characterizes the local movement state of the trajectory. It maps and compresses the direction quadrant information and trajectory curvature information of the trajectory sequence to generate a character sequence, improving the accuracy and robustness of trajectory movement pattern recognition. It compares the sequence templates of different trajectory types with the character sequence to obtain the comparison results. Based on the comparison results, it identifies the trajectory movement pattern of the target object, and can identify a variety of target object trajectory movement patterns with translation and rotation invariance, improving the efficiency and versatility of trajectory movement pattern recognition.

[0101] See Figure 6 As shown, this embodiment of the invention discloses a target object trajectory movement pattern recognition device, which may specifically include:

[0102] The data acquisition and processing module 11 is used to acquire trajectory data of the target object, perform linear interpolation and low-pass filtering smoothing on the trajectory data, and obtain a trajectory sequence.

[0103] The information determination module 12 is used to calculate the instantaneous displacement direction and discrete curvature based on the trajectory sequence, and to determine the trajectory sequence direction quadrant information and trajectory curvature information using the instantaneous displacement direction and the discrete curvature;

[0104] The mapping and compression module 13 is used to map and compress the trajectory sequence direction quadrant information and the trajectory curvature information to generate a character sequence;

[0105] The comparison module 14 is used to construct sequence templates for different trajectory types, compare the sequence templates of different trajectory types with the character sequence, and obtain the comparison result; the sequence template is a template that represents the trajectory movement pattern of the target object;

[0106] The identification module 15 is used to identify the trajectory movement pattern of the target object based on the comparison result.

[0107] In this embodiment, trajectory data of the target object is acquired, and the trajectory data is subjected to linear interpolation and low-pass filtering smoothing to obtain a trajectory sequence. Based on the trajectory sequence, the instantaneous displacement direction and discrete curvature are calculated, and the instantaneous displacement direction and discrete curvature are used to determine the trajectory sequence's direction quadrant information and trajectory curvature information. The trajectory sequence's direction quadrant information and trajectory curvature information are mapped and compressed to generate a character sequence. Sequence templates for different trajectory types are constructed, and the sequence templates for different trajectory types are compared with the character sequence to obtain comparison results. The sequence template is a template representing the trajectory movement pattern of the target object. Based on the comparison results, the trajectory movement pattern of the target object is identified. This application performs linear interpolation and low-pass filtering smoothing on the trajectory data of the target object to obtain a trajectory sequence. This avoids interference caused by uneven trajectory sampling and local drift jitter. While preserving the true motion trend of the trajectory, it suppresses high-frequency noise and eliminates the influence of trajectory sampling drift on subsequent direction calculation and curvature feature extraction. Based on the trajectory sequence, it calculates the instantaneous displacement direction and discrete curvature, and uses the instantaneous displacement direction and discrete curvature to determine the direction quadrant information and trajectory curvature information of the trajectory sequence. It accurately and finely segments and characterizes the local movement state of the trajectory. It maps and compresses the direction quadrant information and trajectory curvature information of the trajectory sequence to generate a character sequence, improving the accuracy and robustness of trajectory movement pattern recognition. It compares the sequence templates of different trajectory types with the character sequence to obtain the comparison results. Based on the comparison results, it identifies the trajectory movement pattern of the target object, and can identify a variety of target object trajectory movement patterns with translation and rotation invariance, improving the efficiency and versatility of trajectory movement pattern recognition.

[0108] In some specific embodiments, the data acquisition and processing module 11 may specifically include:

[0109] The distance calculation module is used to calculate the distance between two adjacent continuous points in latitude and longitude coordinates by using the semi-versus formula to calculate the distance between the trajectory data.

[0110] The linear interpolation module is used to perform linear interpolation on the distance based on a preset first step length to obtain a time-interval trajectory sequence.

[0111] In some specific embodiments, the data acquisition and processing module 11 may specifically include:

[0112] The low-pass filtering and smoothing module is used to perform low-pass filtering and smoothing on the time-equal interval trajectory sequence using a Schwarzky-Gore filter, based on a preset sliding window and a preset second step size, to obtain the trajectory sequence.

[0113] In some specific embodiments, the information determination module 12 may specifically include:

[0114] The instantaneous displacement direction calculation module is used to calculate the instantaneous displacement direction by using the first-order difference approximation derivative on discrete sampled trajectory points in the trajectory sequence.

[0115] The formula for calculating the direction of the instantaneous displacement is:

[0116] ;

[0117] ;

[0118] ;

[0119] in, The instantaneous displacement direction These are the coordinates of discrete sampled trajectory points in the trajectory sequence. To and The next adjacent discrete sampling trajectory point, It is the velocity vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the instantaneous direction of movement.

[0120] In some specific embodiments, the information determination module 12 may specifically include:

[0121] The discrete curvature calculation module is used to calculate the discrete curvature of discrete sampled trajectory points in the trajectory sequence using the second-order difference approximation derivative.

[0122] The formula for calculating the discrete curvature is:

[0123] ;

[0124] ;

[0125] ;

[0126] in, For discrete curvature, Let be the velocity vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the instantaneous direction of movement. To and The velocity vector of the next adjacent discrete sampling trajectory point in the latitude and longitude direction. The acceleration vector of the discrete sampled trajectory points in the latitude and longitude directions represents the degree and direction of curvature.

[0127] In some specific embodiments, the comparison module 14 may specifically include:

[0128] The initial sequence template construction module is used to construct the initial Z-type sequence template, the initial O-type sequence template, and the initial C-type sequence template.

[0129] The sequence template construction module is used to modify the initial Z-type sequence template, the initial O-type sequence template, and the initial C-type sequence template in different directions to construct sequence templates of different trajectory types; the trajectory types include Z-type, O-type, and C-type.

[0130] In some specific embodiments, the comparison module 14 may specifically include:

[0131] The time range setting module is used to set the time range according to business needs;

[0132] The judgment module is used to determine whether there is a target character sequence corresponding to the character sequence in the sequence templates of different trajectory types within the time range.

[0133] In some specific embodiments, the identification module 15 may specifically include:

[0134] The trajectory movement pattern recognition module is used to recognize the trajectory movement pattern of the target object based on the target character sequence if there is a target character sequence corresponding to the character sequence in the sequence templates of different trajectory types within the time range.

[0135] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the target object trajectory movement pattern recognition method performed by the electronic device in any of the foregoing embodiments.

[0136] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0137] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0138] The operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device 20 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. The operating system 221 can be Windows, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the target object trajectory movement pattern recognition method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the target object trajectory movement pattern recognition device from external devices, as well as data collected by its own input / output interface 25.

[0139] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0140] Furthermore, this application also discloses a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements the steps of the target object trajectory movement pattern recognition method disclosed in any of the foregoing embodiments.

[0141] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] The present invention has provided a detailed description of a method, apparatus, device, and storage medium for recognizing the trajectory movement pattern of a target object. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for recognizing the trajectory movement pattern of a target object, characterized in that, include: The trajectory data of the target object is acquired, and the trajectory data is subjected to linear interpolation and low-pass filtering smoothing to obtain a trajectory sequence. The instantaneous displacement direction and discrete curvature are calculated based on the trajectory sequence, and the trajectory sequence direction quadrant information and trajectory curvature information are determined using the instantaneous displacement direction and the discrete curvature; The trajectory sequence direction quadrant information and the trajectory curvature information are mapped and compressed to generate a character sequence; Construct sequence templates for different trajectory types, compare the sequence templates for different trajectory types with the character sequence, and obtain the comparison results; the sequence template is a template representing the trajectory movement pattern of the target object; The trajectory movement pattern of the target object is identified based on the comparison results; Calculating the instantaneous displacement direction based on the trajectory sequence includes: using the first-order difference approximation derivative to calculate the discrete sampled trajectory points in the trajectory sequence to obtain the instantaneous displacement direction; the formula for calculating the instantaneous displacement direction is: ； ； ； in, The instantaneous displacement direction These are the coordinates of discrete sampled trajectory points in the trajectory sequence. To and The next adjacent discrete sampling trajectory point, The velocity vector of the discrete sampled trajectory points in the latitude and longitude directions represents the instantaneous direction of movement; Calculating discrete curvature based on the trajectory sequence includes: using the second-order difference approximation derivative to calculate the discrete sampled trajectory points in the trajectory sequence to obtain the discrete curvature; the formula for calculating the discrete curvature is: ； ； ； in, For discrete curvature, Let be the velocity vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the instantaneous direction of movement. To and The velocity vector of the next adjacent discrete sampling trajectory point in the latitude and longitude direction. The acceleration vector of the discrete sampled trajectory points in the latitude and longitude directions represents the degree and direction of curvature.

2. The target object trajectory movement pattern recognition method according to claim 1, characterized in that, Performing linear interpolation on the trajectory data includes: The distance between two adjacent continuous points in latitude and longitude coordinates is obtained by using the semi-versus formula to calculate the trajectory data. Linear interpolation is performed on the distance based on a preset first step length to obtain a time-interval trajectory sequence.

3. The target object trajectory movement pattern recognition method according to claim 2, characterized in that, The trajectory data is smoothed by low-pass filtering to obtain a trajectory sequence, including: The trajectory sequence is obtained by using a Schwarzky-Gore filter and performing low-pass filtering smoothing on the time-equal interval trajectory sequence based on a preset sliding window and a preset second step size.

4. The target object trajectory movement pattern recognition method according to claim 1, characterized in that, The construction of sequence templates for different trajectory types includes: Construct initial Z-type sequence templates, initial O-type sequence templates, and initial C-type sequence templates; The initial Z-shaped sequence template, the initial O-shaped sequence template, and the initial C-shaped sequence template are varied in different directions to construct sequence templates of different trajectory types; the trajectory types include Z-shaped, O-shaped, and C-shaped.

5. The target object trajectory movement pattern recognition method according to any one of claims 1 to 4, characterized in that, The sequence templates of different trajectory types are compared with the character sequence to obtain the comparison results; Based on the comparison results, the trajectory movement pattern of the target object is identified, including: Set the time range according to business needs; Determine whether a target character sequence corresponding to the character sequence exists in the sequence templates of different trajectory types within the time range; If, within the time range, there exists a target character sequence corresponding to the character sequence in the sequence templates of different trajectory types, then the trajectory movement pattern of the target object is identified based on the target character sequence.

6. A target object trajectory movement pattern recognition device, characterized in that, include: The data acquisition and processing module is used to acquire trajectory data of the target object, perform linear interpolation and low-pass filtering smoothing on the trajectory data, and obtain a trajectory sequence. The information determination module is used to calculate the instantaneous displacement direction and discrete curvature based on the trajectory sequence, and to determine the trajectory sequence direction quadrant information and trajectory curvature information using the instantaneous displacement direction and the discrete curvature; The mapping and compression module is used to map and compress the direction quadrant information and the trajectory curvature information of the trajectory sequence to generate a character sequence; The comparison module is used to construct sequence templates for different trajectory types, compare the sequence templates for different trajectory types with the character sequence, and obtain the comparison results; the sequence template is a template that represents the trajectory movement pattern of the target object; The identification module is used to identify the trajectory movement pattern of the target object based on the comparison result; Calculating the instantaneous displacement direction based on the trajectory sequence includes: using the first-order difference approximation derivative to calculate the discrete sampled trajectory points in the trajectory sequence to obtain the instantaneous displacement direction; the formula for calculating the instantaneous displacement direction is: ； ； ； in, The instantaneous displacement direction These are the coordinates of discrete sampled trajectory points in the trajectory sequence. To and The next adjacent discrete sampling trajectory point, The velocity vector of the discrete sampled trajectory points in the latitude and longitude directions represents the instantaneous direction of movement; Calculating discrete curvature based on the trajectory sequence includes: using the second-order difference approximation derivative to calculate the discrete sampled trajectory points in the trajectory sequence to obtain the discrete curvature; the formula for calculating the discrete curvature is: ； ； ； in, For discrete curvature, Let be the velocity vector of the discrete sampled trajectory points in the latitude and longitude directions, representing the instantaneous direction of movement. To and The velocity vector of the next adjacent discrete sampling trajectory point in the latitude and longitude direction. The acceleration vector of the discrete sampled trajectory points in the latitude and longitude directions represents the degree and direction of curvature.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the target object trajectory movement pattern recognition method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the target object trajectory movement pattern recognition method as described in any one of claims 1 to 5.

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