A micro multi-target tracking method based on double track storage and adaptive threshold

Abstract

The present application relates to computer vision and image processing technical field, specifically to a kind of based on double track storage and adaptive threshold micro multi-target tracking method, comprising the following steps: S1, using Gaussian mixture model to carry out background modeling, extract foreground pixel to generate binary image;S2, it is carried out morphological processing and connected region analysis, and the centroid set that meets area threshold is extracted;S3, establish double track structure, calculate Euclidean distance and according to adaptive threshold centroid association or new track creation;S4, filter insufficient length track, obtain effective track set;S5, effective track visualization, mark start and end point and distinguish color and show target movement path.The present application, through double track storage mechanism, solves the contradiction problem of association calculation efficiency and track integrity, dynamically adjusts association distance according to frame sampling rate, effectively solves track disconnection problem, improves track integrity, recognition accuracy and subsequent analysis practicality.

Description

Technical Field

[0001] This invention relates to the fields of computer vision and image processing technology, and in particular to a method for tracking tiny multi-target objects based on dual trajectory storage and adaptive threshold. Background Technology

[0002] In applications such as multi-target video monitoring and behavior analysis of underwater sonar images, continuous tracking of micro- and small targets has always been an important research direction in the field of computer vision. Due to the high acquisition cost, low efficiency, and limited datasets of underwater sonar images, and the small pixel area and indistinct texture features of micro- and small targets in images, they are easily affected by noise in low-contrast backgrounds, leading to problems such as low image resolution, speckle noise, blurred contours, and degraded motion features. Therefore, motion trajectory modeling methods have become the mainstream approach for small target tracking. However, existing methods face two core problems: first, fixed association thresholds cannot adapt to different frame sampling rates, resulting in frequent trajectory breaks; second, single trajectory storage struggles to balance association calculation efficiency and trajectory integrity.

[0003] Existing methods suffer from the following problems when processing micro-targets in underwater sonar images: First, the fixed association threshold problem: Existing methods typically use a fixed distance threshold for centroid association. When the video frame sampling rate changes, the fixed threshold cannot adapt to the maximum displacement changes at different frame intervals, leading to frequent trajectory breaks. Second, the trajectory storage contradiction: Existing methods only maintain single trajectory information. If only the last centroid is retained for fast association calculation, historical trajectory information is lost, compromising data integrity. If all centroid information is retained to ensure integrity, the association calculation time overhead increases, impacting real-time performance, making it difficult to strike a balance between efficiency and integrity. Furthermore, due to fluctuations in detection accuracy, micro-targets are prone to inter-frame loss, resulting in discontinuous trajectories. Summary of the Invention

[0004] This invention provides a method for tracking small multi-target targets based on dual trajectory storage and adaptive thresholding. Addressing the problem of frequent trajectory breakage due to fixed association thresholds in existing methods failing to adapt to different frame sampling rates, an adaptive association threshold mechanism is employed. This mechanism dynamically adjusts the association distance threshold based on the frame sampling interval, effectively resolving the trajectory breakage issue. To address the trade-off between association calculation efficiency and trajectory integrity in existing methods, a dual trajectory storage structure is established: the real-time trajectory retains only the last valid centroid for rapid association calculation, while the complete trajectory retains all centroid information (including NaN placeholders) to ensure data integrity. A synchronous update mechanism achieves a balance between efficiency and integrity. Through this dual trajectory storage mechanism and adaptive thresholding mechanism, combined with foreground extraction, morphological processing, connected component analysis, and trajectory filtering, robust tracking of small underwater multi-target targets is achieved, significantly improving trajectory integrity, association accuracy, and real-time performance.

[0005] A method for tracking small multi-target objects based on dual trajectory storage and adaptive thresholding includes the following steps: S1. A Gaussian mixture model is used to model the background of the input image. Foreground pixels are extracted based on the matching relationship between pixels and the background model to generate a binary foreground image. S2, perform opening, closing and hole filling operations on the binary foreground image to obtain the final foreground image, perform connected region analysis on it, calculate the area and centroid coordinates of each foreground region, retain the foreground regions whose area is not less than the set minimum area threshold, and extract the corresponding centroid set. S3. Establish a dual trajectory storage structure for each target, including real-time trajectory and complete trajectory. Calculate the Euclidean distance between the current centroid and the last centroid of each trajectory, and calculate the adaptive association threshold to complete centroid association or new trajectory creation. If no associated centroid is detected in the current frame, add missing values ​​to the real-time trajectory and complete trajectory respectively. S4. Filter the complete trajectory by length threshold, count the number of valid centroids for each complete trajectory, and retain only the trajectories with a valid length not less than the minimum length threshold to obtain the set of valid complete trajectories. S5 visualizes the complete set of valid trajectories, marks the start and end points and assigns colors, and is used to observe the target's movement path and tracking effect.

[0006] Optionally, S1 includes: S11, for Current pixel at time step Its probability density function is defined as: In the formula: For the first Each Gaussian component in The weight of time, satisfying ; It is the mean vector; Let covariance matrix be the variance matrix. Standard deviation, It is an identity matrix.

[0007] S12, pixels The probability density function of a single Gaussian component is: ;in The data dimension determines the function. Mathematical structure: for dimensional vector, for Matrix. In this study, data dimensions After the model parameters are initialized, the Gaussian component suitable for the pixel is determined through parameter updates.

[0008] S13, Background model selection adopts stability index The background component set is defined as: ;in T Select a threshold for the background. For the current pixel. X t If it matches B set If any component in the image is not identified, it is classified as a background pixel; otherwise, it is classified as a foreground pixel, resulting in a foreground binary image. ,in Indicates the foreground pixel. This represents the background pixels. With all parameters remaining relatively stable, the classification of foreground and background pixels is completed, and the statistical distribution of each pixel is estimated through online updates.

[0009] Optionally, S2 includes: S21, for the foreground binary image Perform morphological processing, including opening, closing, and hole filling, to generate the final foreground binary image. ; S22, Final Foreground Image Connectivity components are labeled using the 4-connectivity or 8-connectivity criteria to obtain... A set of independent connected components For each connected region Calculate its area and centroid coordinates ; S23, Set minimum area threshold , retain satisfaction Connected regions This constitutes a set of effective connected regions. And extract the corresponding set of valid centroids. .

[0010] Optionally, S21 includes: S211, for the input foreground binary image Perform the opening operation, using a size of rectangular structural elements Remove noise and small protrusions from the image to obtain the intermediate image after the opening operation. ; S212, the intermediate image after the opening operation. Perform the closing operation, using a size of rectangular structural elements Fill in small depressions and smooth target edges to obtain the image after the closing operation. ; S213, the image after the closing operation A hole-filling operation is performed using iterative dilation and image inversion intersection to fill the holes inside the target area, resulting in the final foreground image. .

[0011] Optionally, S3 includes: S31 establishes a dual trajectory storage structure for each target, including real-time trajectory. and complete trajectory ,in For trajectory indexing, Indexing for time frames; real-time trajectory Only the last valid centroid is retained for fast correlation calculation, complete trajectory Retain all centroid information (including NaN placeholders) to ensure data integrity; S32, for the centroid detected in the current frame Calculate its relationship with each existing trajectory The last effective centroid Euclidean distance Based on adaptive association threshold (in Based on the threshold, Determine the current centroid (within the frame sampling interval). Is it related to the trajectory? Related; if If so, the association is considered successful; S33, when the center of mass Successfully linked to the trajectory At that time, the newly detected centroid Synchronized updates to both real-time and complete tracks: , When all At that time, a new trajectory is created for the current centroid: , and update the total number of trajectories. = If the current frame has no associated centroid, then set... =NaN, add missing value placeholders to both the real-time trajectory and the complete trajectory to maintain time synchronization; Optionally, S31 includes: S311, establish a dual trajectory storage structure for each detected target and assign trajectory indexes. Including real-time trajectory and complete trajectory ,in t Indexing for time frames; real-time trajectory Only the last valid centroid is retained for fast correlation calculation, complete trajectory All centroid information (including NaN placeholders) is preserved to ensure data integrity; when a target is detected for the first time, its centroid coordinates in the current frame are written as initial values ​​into the real-time trajectory and the complete trajectory, and a trajectory index is assigned. S312, in each time frame In this process, all established target trajectories are stored and managed, specifically including: Real-time trajectory Only record the trajectory In the previous frame The final centroid coordinates in the data; Complete trajectory Updated sequentially according to time frames, recording the trajectory. In each frame, if no matching target is detected in a certain frame, a missing value is written at the corresponding position in that frame to maintain the continuity of the trajectory structure on the time axis.

[0012] Optionally, S32 includes: S321, for each centroid detected in the current frame Calculate its relationship with all established trajectories. The final center of mass Euclidean distance between ; S322, based on the basic correlation distance threshold set during trajectory processing. Sampling interval with the current frame Calculate the adaptive correlation threshold ; S323, if the current centroid With trajectory satisfy Then it is considered that the centroid and the trajectory If the connection is successful, the centroid will be considered an unconnected target and the new trajectory creation process will begin.

[0013] Optionally, S4 includes: S41, for the complete trajectory set Each complete trajectory in Extract its effective centroids to form a set. ,in The total number of trajectories is represented by NaN, which is a placeholder for missing values ​​added when no valid target is detected in the frame. S42, count the number of valid centroids in each complete trajectory, and use this as the effective length of the complete trajectory. ; S43, Set minimum length threshold Retain all that meet the requirements The complete trajectory is obtained, resulting in a filtered set of valid complete trajectories. .

[0014] Optionally, S5 includes: S51, for the filtered set of valid complete trajectories Each trajectory is assigned a unique color code for visual differentiation, and a green dot is drawn at the starting centroid of each complete trajectory, and a red square is drawn at the ending centroid to clearly indicate the start and end positions of the target's motion. S52, based on the temporal centroid coordinate sequence of each complete trajectory, draws the target motion path image, and overlays all complete trajectories to form a trajectory visualization map, intuitively showing the target's motion trajectory shape, direction change, displacement range and aggregation distribution.

[0015] The beneficial effects of this invention are: This invention employs an adaptive association threshold mechanism, dynamically adjusting the association distance threshold based on the frame sampling interval. This effectively solves the problem of frequent trajectory breakage caused by a fixed threshold failing to adapt to different frame sampling rates. Experimental results show that this method reduces the trajectory breakage rate to 0.9% and increases the association accuracy to 96.19% (compared to 45.56% for the fixed threshold method), significantly improving trajectory integrity and association accuracy.

[0016] This invention establishes a dual trajectory storage structure. For real-time trajectories, only the last valid centroid is retained for rapid correlation calculation, while the complete trajectory retains all centroid information to ensure data integrity. This effectively resolves the contradiction between correlation calculation efficiency and trajectory integrity. Experimental results show that, while ensuring 100% trajectory integrity, the average correlation time is only 0.255 ms / frame, far below the real-time target threshold of 1.0 ms / frame, achieving a balance between efficient real-time correlation and data integrity.

[0017] This invention, by visualizing a set of effective trajectories, marking the start and end points of the trajectories and encoding their colors, not only helps to intuitively display the motion paths, directional changes, and spatial distribution characteristics of multiple targets, but can also be used for underwater tracking algorithm performance verification and target behavior pattern analysis. It provides high-quality data support for tasks such as multi-target behavior modeling and clustering recognition in applications such as underwater sonar image multi-target motion trajectory detection and tracking in marine ecological monitoring, fishery resource assessment, and autonomous navigation of underwater robots. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this 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 for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the tracking method according to an embodiment of the present invention; Figure 2 Schematic diagram of an underwater target sonar measurement experimental platform; Figure 3 Sonar images of the target to be tested in the experiment; Figure 4 Diagram showing the foreground modeling and processing, and the three-step morphological post-processing procedure; Figure 5 Image showing the results of the Blob detection; Figure 6 This is a Blob area statistics chart (the red dashed line represents the minimum area threshold). Figure 7 The graph shows the performance analysis of the target association algorithm. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0021] like Figure 1-7 As shown, a method for tracking small multi-target objects based on dual trajectory storage and adaptive thresholding includes the following steps: S1. A Gaussian mixture model is used to model the background of the input image. Foreground pixels are extracted based on the matching relationship between pixels and the background model to generate a binary foreground image. S2, perform opening, closing and hole filling operations on the binary foreground image to obtain the final foreground image, perform connected component analysis on it, calculate the area and centroid coordinates of each foreground region, retain the foreground regions whose area is not less than the set minimum area threshold, and extract the corresponding centroid set. S3. Establish a dual trajectory storage structure for each target, including real-time trajectory and complete trajectory. Calculate the Euclidean distance between the current centroid and the last centroid of each trajectory, and calculate the adaptive association threshold to complete centroid association or new trajectory creation. If no associated centroid is detected in the current frame, add missing values ​​to the real-time trajectory and complete trajectory respectively. S4. Filter the complete trajectory by length threshold, count the number of valid centroids for each complete trajectory, and retain only the trajectories with a valid length not less than the minimum length threshold to obtain the set of valid complete trajectories. S5 visualizes the complete set of valid trajectories, marks the start and end points and assigns colors, and is used to observe the target's movement path and tracking effect.

[0022] S1 includes: S11, for Current pixel at time step Its probability density function is defined as: In the formula: For the first Each Gaussian component in The weight of time, satisfying ; It is the mean vector; Let covariance matrix be the variance matrix. Standard deviation, It is an identity matrix.

[0023] S12, pixels The probability density function of a single Gaussian component is: ;in The data dimension determines the function. Mathematical structure: for dimensional vector, for Matrix. In this study, data dimensions After the model parameters are initialized, the Gaussian component suitable for the pixel is determined through parameter updates.

[0024] S13, Background model selection adopts stability index The background component set is defined as: ;in T Select a threshold for the background. For the current pixel. X t If it matches B set If any component in the image is not identified, it is classified as a background pixel; otherwise, it is classified as a foreground pixel, resulting in a foreground binary image. ,in Indicates the foreground pixel. This represents the background pixels. With all parameters remaining relatively stable, the classification of foreground and background pixels is completed, and the statistical distribution of each pixel is estimated through online updates.

[0025] S2 includes: S21, for the foreground binary image Perform morphological processing, including opening, closing, and hole filling, to generate the final foreground binary image. ; S22, Final Foreground Image Connectivity components are labeled using the 4-connectivity or 8-connectivity criteria to obtain... A set of independent connected components For each connected region Calculate its area and centroid coordinates ; ; ; in, For the first The total number of pixels in each connected region Connected region Pixel coordinates in; S23, Set minimum area threshold , retain satisfaction Connected regions This constitutes a set of effective connected regions. And extract the corresponding set of valid centroids. .

[0026] S21 includes: S211, for the input foreground binary image Perform an opening operation (erosion followed by dilation) using a size of rectangular structural elements Remove noise and small protrusions from the image to obtain the intermediate image after the opening operation. , is represented as: ; in, For morphological erosion operations, For morphological dilation operations; S212, the intermediate image after the opening operation. Perform the closing operation (dilation followed by erosion), using a size of rectangular structural elements Fill in small depressions and smooth target edges to obtain the image after the closing operation. , is represented as: ; S213, the image after the closing operation ,Right now Perform hole filling operation. for The supplement, for The iterative process for filling holes in a rectangular structural element is as follows:

[0027] In the formula: As the initial seed image, For the first The binary image after the next iteration. This iterative process expands the filled region through dilation and is then compared with the image complement. Find the intersection. Iterate until convergence. At this time from Extract all pixels with a value of 1; the regions formed by these pixel positions are the filled hole regions. Merge the filled hole regions with the morphologically filtered foreground binary image to obtain the final foreground image. .

[0028] S3 includes: S31 establishes a dual trajectory storage structure for each target, including real-time trajectory. and complete trajectory ,in For trajectory indexing, Indexing for time frames; real-time trajectory Only the last valid centroid is retained for fast correlation calculation, complete trajectory Retain all centroid information (including NaN placeholders) to ensure data integrity; S32, for the centroid detected in the current frame Calculate its relationship with each existing trajectory The last effective centroid Euclidean distance Based on adaptive association threshold (in Based on the threshold, Determine the current centroid (within the frame sampling interval). Is it related to the trajectory? Related; if If so, the association is considered successful; S33, when the center of mass With trajectory When the association is successful, the new detection centroid will be... Synchronized updates to both real-time and complete tracks, when all When a new trajectory is created for the current centroid, if no associated centroid is detected in the current frame, then... NaN is used, and missing values ​​are added as placeholders to both the real-time trajectory and the complete trajectory to maintain the synchronization consistency of the trajectory on the time axis, as shown below: ; .

[0029] S31 includes: S311, for each detected target, establish a trajectory index. The identifier features a dual trajectory storage structure, including real-time trajectories. With complete trajectory When a target is detected for the first time, the centroid coordinates of the target in the current frame are written as initial values ​​into the real-time trajectory and the complete trajectory, and the trajectory index is assigned. S312, in each time frame In this process, all established target trajectories are stored and managed, specifically including: Real-time trajectory Only record the trajectory In the previous frame The final centroid coordinates in the data; Complete trajectory Updated sequentially according to time frames, recording the trajectory. In each frame, if no matching target is detected in a certain frame, a missing value is written at the corresponding position in that frame to maintain the continuity of the trajectory structure on the time axis.

[0030] S32 includes: S321, for each centroid detected in the current frame Calculate its relationship with all established trajectories. The final center of mass Euclidean distance between , is represented as: ; in, For trajectory The last centroid coordinates recorded in the previous time frame; S322, based on the basic correlation distance threshold set during trajectory processing. Sampling interval with the current frame Calculate the adaptive correlation threshold , is represented as: ; S323, if the current centroid With trajectory satisfy Then it is considered that the centroid and the trajectory If the connection is successful, the centroid will be considered an unconnected target and the new trajectory creation process will begin.

[0031] S4 includes: S41, for the complete trajectory set Each complete trajectory in Extract its effective centroids to form a set. ,in The total number of trajectories is represented by NaN, which is a placeholder for missing values ​​added when no valid target is detected in the frame. S42, count the number of valid centroids in each complete trajectory, and use this as the effective length of the complete trajectory. , is represented as: ; in, for The number of elements in the middle; S43, Set minimum length threshold Retain all that meet the requirements The complete trajectory; S5 includes: S51. For each trajectory in the filtered valid complete trajectory, a unique color code is assigned for visual differentiation, and a green dot is drawn at the starting centroid position of each complete trajectory, and a red square is drawn at the ending centroid position to clearly indicate the starting and ending positions of the target's motion. S52, based on the temporal centroid coordinate sequence of each complete trajectory, draws the target motion path image, and overlays all complete trajectories to form a trajectory visualization map, intuitively showing the target's motion trajectory shape, direction change, displacement range and aggregation distribution.

[0032] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0033] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for tracking small multi-target objects based on dual trajectory storage and adaptive thresholding, characterized in that, Includes the following steps: S1. A Gaussian mixture model is used to model the background of the input image. Foreground pixels are extracted based on the matching relationship between pixels and the background model to generate a foreground binary image. S2, perform opening, closing and hole filling operations on the foreground binary image to obtain the final foreground image, perform connected region analysis on it, calculate the area and centroid coordinates of each foreground region, retain the foreground regions whose area is not less than the set minimum area threshold, and extract the corresponding centroid set. S2 includes: S21, for the foreground binary image Perform morphological processing, including opening, closing, and hole filling, to generate the final foreground binary image. ; S21 includes: S211, for the input foreground binary image Perform the opening operation, using a size of rectangular structural elements SE 1. Remove noise and small protrusions from the image to obtain the intermediate image after the opening operation. ; S212, the intermediate image after the opening operation. Perform the closing operation, using a size of rectangular structural elements SE 2. Fill in the small depressions and smooth the target edges to obtain the image after the closing operation. ; S213, the image after the closing operation A ,Right now A hole-filling operation is performed using iterative dilation and image inversion intersection to fill the holes within the target region, resulting in the final foreground image. ; S22, Final Foreground Image Connectivity components are labeled using the 4-connectivity or 8-connectivity criteria to obtain... N A set of independent connected components R For each connected region Calculate its area and centroid coordinates ; S23, Set minimum area threshold , retain satisfaction Connected regions This constitutes a set of effective connected regions. And extract the corresponding set of valid centroids. ; S3. Establish a dual trajectory storage structure for targets in the effective centroid set. The dual trajectory storage structure includes real-time trajectory and complete trajectory. The real-time trajectory retains only the last effective centroid for fast association calculation with newly detected centroids in the current frame. The complete trajectory retains all centroid information to ensure data integrity. The complete trajectory saves the centroid coordinates of the corresponding trajectory in each frame according to the time frame order. When no target matching the corresponding trajectory is detected in a frame, a NaN missing value is written at the corresponding position in that frame to maintain the continuity of the complete trajectory on the time axis. Calculate the Euclidean distance between the newly detected centroid and the existing trajectory, perform centroid association or create a new trajectory according to the adaptive threshold, and synchronously update the real-time trajectory and complete trajectory. If there is no associated centroid in the current frame, add a NaN placeholder to the real-time trajectory and complete trajectory. S3 includes: S31 establishes a dual trajectory storage structure for each target, including real-time trajectory. and complete trajectory Where m is the trajectory index and t is the time frame index; real-time trajectory Only the last valid centroid is retained for fast correlation calculation, complete trajectory Retain all centroid information to ensure data integrity; specifically including: S311, establish a dual trajectory storage structure for each detected target, and assign a trajectory index m, including the real-time trajectory. and complete trajectory ,in t Indexing for time frames; real-time trajectory Only the last valid centroid is retained for fast correlation calculation, complete trajectory All centroid information is preserved to ensure data integrity; when a target is detected for the first time, its centroid coordinates in the current frame are written as initial values ​​into the real-time trajectory and the complete trajectory, and a trajectory index is assigned. S312, in each time frame In this process, all established target trajectories are stored and managed, specifically including: Real-time trajectory Only record the coordinates of the last valid centroid of trajectory m; Complete trajectory Update sequentially according to the time frame order, record the centroid coordinates of trajectory m in each frame. If no matching target is detected in a certain frame, write the missing value at the corresponding position in that frame to maintain the continuity of the trajectory structure on the time axis. S32, for the centroid detected in the current frame Calculate its relationship with the last valid centroid of each existing trajectory. Euclidean distance Based on adaptive association threshold ,in Based on the threshold, Determine the current centroid at the frame sampling interval. Is it associated with trajectory m? If If so, the association is considered successful; S32 includes: S321, for each centroid detected in the current frame Calculate its final centroid with all established trajectories m. Euclidean distance between ; S322, based on the basic correlation distance threshold set during trajectory processing. Sampling interval with the current frame Calculate the adaptive correlation threshold ; S323, if the current centroid The trajectory m satisfies If the centroid is successfully associated with trajectory m, then the centroid is considered to be successfully associated with trajectory m; otherwise, the centroid will be regarded as an unassociated target and the new trajectory creation process will begin. S33, when the center of mass When successfully associated with trajectory m, the newly detected centroid will be... Synchronized updates to both real-time and complete tracks: When all times Create a new trajectory for the current centroid: and update the total number of trajectories. If the current frame has no associated centroid, then set... Placeholders for missing values ​​are added to both the real-time trajectory and the complete trajectory to maintain time synchronization. S4. Filter the complete trajectory with a length threshold, count the number of valid centroids that are not NaN in each complete trajectory as the effective length of the trajectory, and retain only the trajectories whose effective length is not less than the minimum length threshold to obtain the set of valid complete trajectories. S4 includes: S41, for the complete trajectory set Each complete trajectory Extract its effective centroids to form a set. ,in The total number of trajectories is represented by NaN, which is a placeholder for missing values ​​added when no valid target is detected in the frame. S42, count the number of valid centroids in each complete trajectory, and use this as the effective length of the complete trajectory. ; S43, Set minimum length threshold Retain all that meet the requirements The complete trajectory; S5 visualizes the complete set of valid trajectories, marks the start and end points and assigns colors, and is used to observe the target's motion path and tracking effect. S5 includes: S51. For each trajectory in the filtered valid complete trajectory, a unique color code is assigned for visual differentiation, and a green dot is drawn at the starting centroid position of each complete trajectory, and a red square is drawn at the ending centroid position to clearly indicate the starting and ending positions of the target's motion. S52, based on the temporal centroid coordinate sequence of each complete trajectory, draws the target motion path image, and overlays all complete trajectories to form a trajectory visualization map, intuitively showing the target's motion trajectory shape, direction change, displacement range and aggregation distribution.

Images