A northeast tiger individual left and right side image association method based on space-time information

Abstract

A northeast tiger individual left and right side image association method based on space-time information belongs to the field of animal recognition, comprising: collecting and labeling the data of the northeast tiger individual shot by a wildlife camera; predicting the key point information and body side direction of the northeast tiger individual based on a key point detection algorithm; estimating the individual information of the northeast tiger based on an individual recognition algorithm; and associating the left and right body side images of the northeast tiger individual in the image library based on the space-time information. In the present application, the northeast tiger individual after the left and right side image association has bilateral information, so that even if only one side is shot in the wild, the correct northeast tiger individual can be accurately matched, and the detection accuracy is improved; by associating the left and right side images of the northeast tiger individual, the space-time information of the northeast tiger is fully utilized, so that a solid data foundation can be provided for population dynamic monitoring, and the population quantity and home range can be more accurately estimated; the present application does not need to rely on artificial expert experience, the detection time is shortened, and multi-person collaborative detection operation can be realized, so that the operation is convenient.

Description

Technical Field

[0001] This invention belongs to the field of animal recognition technology, specifically relating to a method for associating left and right side images of a Siberian tiger based on spatiotemporal information. Background Technology

[0002] The Siberian tiger is one of the world's most endangered large cat species. Accurately distinguishing and identifying every Siberian tiger in the wild is not only a cutting-edge scientific research practice, but also a core task concerning species survival, ecological balance and conservation effectiveness, and has vital and far-reaching significance.

[0003] Current computer vision-based methods for identifying Siberian tigers typically leverage the unique lateral markings of individual tigers, using neural networks to extract features from lateral images and predict individual information. However, this method treats images of the same Siberian tiger from both sides as two distinct individuals, making the results unsuitable for analyzing population size, territory, or other information. To address this, two wildlife cameras can be deployed at each monitoring point in the wild, capturing data from both sides of the tiger simultaneously when it appears. However, this approach incurs significant equipment and deployment costs. Another approach involves manual video analysis, linking left and right images of the same tiger based on the time, scene, and physical characteristics of its appearance. However, this method relies heavily on expert experience and is inefficient. Therefore, providing a method for automatically linking left and right images of the same Siberian tiger in a single-camera deployment scenario is a pressing technical problem for those skilled in the art.

[0004] Existing methods for identifying individual Siberian tigers have the following problems:

[0005] (1) The left and right images of the same Siberian tiger individual are regarded as different individuals, resulting in low detection accuracy;

[0006] (2) Manual association relies on expert experience, which is time-consuming and cannot achieve multi-person collaboration;

[0007] (3) The territorial characteristics of Siberian tigers and the spatiotemporal information of their activities were not fully utilized. Summary of the Invention

[0008] To address the problems of existing methods for identifying individual Siberian tigers, such as treating the left and right images of the same individual as different individuals, relying on expert experience for manual association which is time-consuming and cannot achieve multi-person collaboration, and not fully utilizing spatiotemporal information, this invention provides a method for associating the left and right images of an individual Siberian tiger based on spatiotemporal information.

[0009] The technical solution adopted by this invention to solve the technical problem is as follows:

[0010] This invention provides a method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information, which specifically includes the following steps:

[0011] Step S1: Collect and label data on individual Siberian tigers captured by wildlife cameras;

[0012] Step S2: Predict key point information and body orientation of an individual Siberian tiger based on a key point detection algorithm;

[0013] Step S3: Estimate individual information of Siberian tigers based on individual identification algorithms;

[0014] Step S4: Combine spatiotemporal information to associate Siberian tiger individuals in the left and right side image databases.

[0015] Furthermore, the specific implementation process of step S1 is as follows:

[0016] S1.1: Collect images of individual Siberian tigers captured by wildlife conservation cameras;

[0017] S1.2: Label the location and key information of individual Siberian tigers in the images, and assign individual numbers;

[0018] S1.3: Collect information on the time when individual images of Siberian tigers were captured and the corresponding wildlife protection camera numbers;

[0019] S1.4: Collect latitude, longitude, and attitude information of the installation location of the field protection camera.

[0020] Furthermore, the specific implementation process of step S2 is as follows:

[0021] S2.1: Construct a Siberian tiger keypoint detection dataset based on the location and keypoint information of labeled Siberian tiger individuals;

[0022] S2.2: Based on the keypoint detector, the Siberian tiger keypoint detection dataset is used to predict the side view image of an individual Siberian tiger, and the location and keypoint information of the individual Siberian tiger are obtained.

[0023] S2.3: Construct a database of images of the left side of a Siberian tiger and an image database of images of the right side of a Siberian tiger based on the predicted images of the tiger's lateral side.

[0024] Furthermore, the location information of the marked Siberian tiger individual is the information of the bounding box containing the region of the Siberian tiger individual.

[0025] Furthermore, there are six key points for the marked Siberian tiger individual: key point 1 is the connection between the head and body of the Siberian tiger individual; key point 2 is the connection between the rump and tail of the Siberian tiger individual; key points 3 to 6 are the connection points between the left forelimb, right forelimb, left hindlimb, right hindlimb and body of the Siberian tiger individual, respectively.

[0026] Furthermore, by comparing the x-coordinates of key point 1 and key point 2, the side orientation of the Siberian tiger in the side view image is determined; if key point 1 is to the left of key point 2, then the side view image of the Siberian tiger is the left side view image of the Siberian tiger; if key point 1 is to the right of key point 2, then the side view image of the Siberian tiger is the right side view image of the Siberian tiger.

[0027] Furthermore, the specific implementation process of step S3 is as follows:

[0028] S3.1: Based on the location and key point information of the labeled Siberian tiger individuals, the torso region image is extracted from the left side body image of the Siberian tiger individual, and the torso region image is associated with the labeled individual number;

[0029] S3.2: After horizontally flipping the right-side body image in the image database of the right side of the Siberian tiger individual, merge it with the left-side body image in the image database of the left side of the Siberian tiger individual. The images in the merged image database are used as the training dataset for Siberian tiger individual recognition.

[0030] S3.3: Construct an individual recognition network model and extract features from the images of the Siberian tiger's lateral trunk region on the Siberian tiger individual recognition training dataset, and then use the classification module to predict the individual information of the Siberian tiger.

[0031] Furthermore, the specific implementation process of step S4 is as follows:

[0032] S4.1: Associate the captured images of individual Siberian tigers with the labeled individual ID information, as well as the wildlife camera IDs and the time information of being captured as statistically analyzed in step S1.3, to construct a Siberian tiger spatiotemporal information database.

[0033] S4.2: Obtain the latitude and longitude information of the wildlife camera based on the positioning module inside the wildlife camera, and calculate the relative distance matrix of the wildlife camera based on the latitude and longitude information. , For the number of wildlife conservation cameras, Indicates the number is Wildlife camera and number The distance of the wildlife camera;

[0034] S4.3: Constructing an individual spatiotemporal matrix of the left side body image of an individual Siberian tiger based on the Siberian tiger spatiotemporal information database. Individual spatiotemporal matrix of the right side body image of an individual Siberian tiger , This represents the number of Siberian tiger individuals in the database containing images of the left side of their bodies. This represents the number of Siberian tiger individuals in the database containing images of the right side of their bodies. The number of weeks the wildlife camera worked. This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. Was the Siberian tiger individual numbered in week k? The value is 0 when the wildlife protection camera captures the image and 1 when the image is captured. This indicates that the image of the right side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual Is Zhou numbered? The value is 0 when the wildlife protection camera captures the image and 1 when the image is captured.

[0035] S4.4: Constructing the cost matrix based on the individual spatiotemporal matrix of the left side image of an individual Siberian tiger, the individual spatiotemporal matrix of the right side image of an individual Siberian tiger, and the relative distance matrix of the wildlife camera. Construct an extended cost matrix;

[0036] S4.5: Solve the extended cost matrix using the Hungarian matching algorithm.

[0037] Furthermore, the specific implementation process of step S4.4 is as follows:

[0038] By iterating through the spatiotemporal matrices of images of the left and right sides of an individual Siberian tiger over time, and at weekly intervals, the distance cost sets of all Siberian tigers simultaneously photographed within any given time period are calculated. Its mathematical expression is as follows:

[0039]

[0040] in, This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The distance cost of an individual Siberian tiger This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual Zhou was numbered The wildlife conservation camera captured the image. This indicates that the image of the right side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual Zhou was numbered The wildlife conservation camera captured the image. Indicates the number is Wildlife camera and number The distance of the wildlife camera;

[0041] The cost matrix is ​​calculated based on the distance cost set, and its mathematical expression is as follows:

[0042]

[0043] in, This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The matching cost of individual Siberian tigers; This indicates the distance between the two farthest wildlife cameras;

[0044] Constructing an extended cost matrix , Its mathematical expression is as follows:

[0045] .

[0046] Furthermore, the specific implementation process of step S4.5 is as follows:

[0047] Based on the output of the Hungarian matching algorithm, for each matching pair ,if and Then, the image database containing the left side image of the Siberian tiger will be numbered as follows: The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The Siberian tiger individuals are associated with the same Siberian tiger individual; if and This indicates that the image number in the database for the left side of the Siberian tiger is [number missing]. No corresponding image number for the Siberian tiger individual was found in the Siberian tiger right side body image database. Individual Siberian tigers; if and This indicates that the image number in the database for the right side of the Siberian tiger is [number missing]. No corresponding image number for the Siberian tiger individual was found in the Siberian tiger left side body image database. An individual Siberian tiger.

[0048] The beneficial effects of this invention are:

[0049] (1) Improved detection accuracy: In this invention, the Siberian tiger individual after the left and right side images are associated has bilateral information. Even if only one side is photographed in the wild, researchers can accurately match the correct Siberian tiger individual, thus improving the detection accuracy.

[0050] (2) In this invention, by linking the left and right side images of an individual Siberian tiger, the spatiotemporal information of the Siberian tiger is fully utilized, which can provide a solid data foundation for population dynamic monitoring. Researchers can more accurately estimate the population size and calculate the home range.

[0051] (3) This invention does not rely on human expert experience, shortens the detection time, and enables multi-person collaborative detection operation, making it convenient to operate. Attached Figure Description

[0052] Figure 1 The flowchart illustrates a method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information, as provided by this invention.

[0053] Figure 2 A diagram illustrating key information about the Siberian tiger. Detailed Implementation

[0054] This invention provides a method for associating left and right side images of a Siberian tiger individual based on spatiotemporal information. First, data of Siberian tiger individuals captured by wildlife cameras are collected and labeled. Then, key point detection algorithms are used to predict the key point information and body orientation of the Siberian tiger individual. Individual information of the Siberian tiger is estimated based on an individual recognition algorithm. Finally, spatiotemporal information is combined to associate Siberian tiger individuals in the left and right side image database.

[0055] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0056] See Figure 1 As shown, the present invention provides a method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information. The specific implementation process is as follows:

[0057] Step S1: Collect and label data on individual Siberian tigers captured by wildlife cameras;

[0058] S1.1: Collect images of individual Siberian tigers captured by wildlife conservation cameras.

[0059] S1.2: Label the location and key information of individual Siberian tigers in the images, and assign individual numbers.

[0060] S1.3: Statistics on the time information of individual Siberian tiger images being captured and the corresponding wildlife protection camera numbers.

[0061] S1.4: Collect the latitude, longitude, and attitude information of the installation location of the field protection camera.

[0062] Step S2: Predict key point information and body orientation of an individual Siberian tiger based on a key point detection algorithm;

[0063] S2.1: Based on the location and key point information of the Siberian tiger individuals marked in step S1.2, construct a Siberian tiger key point detection dataset.

[0064] S2.2: Using a keypoint detector (a neural network-based keypoint detector, including but not limited to PoseNet, HRNet, yolov8-pose, etc.), the Siberian tiger keypoint detection dataset is used to predict the side profile image of an individual Siberian tiger, obtaining the location and keypoint information of the individual tiger. Specifically, such as... Figure 2 As shown, the location information is the information of the bounding box containing the region of the Siberian tiger individual; there are six key points: key point 1 is the connection between the head and body of the Siberian tiger individual; key point 2 is the connection between the rump and tail of the Siberian tiger individual; key points 3 to 6 are the connections between the left forelimb, right forelimb, left hindlimb, and right hindlimb of the Siberian tiger individual and the body, respectively. By comparing the x-coordinates of key point 1 and key point 2, the side orientation of the Siberian tiger individual in the side-view image is determined. If key point 1 is to the left of key point 2, the side-view image of the Siberian tiger individual is the left side of the Siberian tiger individual; if key point 1 is to the right of key point 2, the side-view image of the Siberian tiger individual is the right side of the Siberian tiger individual.

[0065] S2.3: Construct a database of images of the left side of a Siberian tiger and an image database of images of the right side of a Siberian tiger based on the images of the side of the tiger predicted in step S2.2.

[0066] Step S3: Estimate individual information of Siberian tigers based on individual identification algorithms;

[0067] S3.1: First, based on the location and key point information of the Siberian tiger individual marked in step S1.2, the torso region image is extracted from the left side body image of the Siberian tiger individual, and then the torso region image is associated with the individual number marked in step S1.2.

[0068] S3.2: Assume that the database contains images of the left side of a Siberian tiger. Only, individual number is The database contains images of the right side of an individual Siberian tiger. Only, individual number is By horizontally flipping the right-side body image from the Siberian tiger individual image database and merging it with the left-side body image from the Siberian tiger individual image database, the desired result can be obtained. Images from the merged image database, representing individual Siberian tigers, were used as the training dataset for Siberian tiger identification.

[0069] S3.3: Construct an individual recognition network model (a classification model based on neural networks, including but not limited to ResNet, VIT, etc.). Use this individual recognition network model to extract features of the Siberian tiger's lateral trunk region image on the Siberian tiger individual recognition training dataset. Then, through the classification module (the classification module is part of the individual recognition network model, which is the last classification layer of the network, including fully connected layers and softmax layers: generally designed so that after feature output, a fully connected layer is connected, and then a softmax layer is connected to output the confidence score of each individual) to predict the Siberian tiger individual information.

[0070] Step S4: Combine spatiotemporal information to associate Siberian tiger individuals in the left and right side image databases;

[0071] S4.1: Associate the captured images of individual Siberian tigers with the individual ID information marked in step S1.2 and the wildlife camera IDs and time information captured in step S1.3 to construct a Siberian tiger spatiotemporal information database.

[0072] S4.2: Obtain the latitude and longitude information of the wildlife camera based on the positioning module inside the wildlife camera, and calculate the relative distance matrix of the wildlife camera based on the latitude and longitude information. ,in For the number of wildlife conservation cameras, Indicates the number is Wildlife camera and number The distance of the wildlife protection camera.

[0073] S4.3: Constructing an individual spatiotemporal matrix of the left side body image of an individual Siberian tiger based on the Siberian tiger spatiotemporal information database. Individual spatiotemporal matrix of the right side body image of a Siberian tiger ,in, This represents the number of Siberian tiger individuals in the database containing images of the left side of their bodies. This represents the number of Siberian tiger individuals in the database containing images of the right side of their bodies. The number of weeks the wildlife camera worked. This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. Was the Siberian tiger individual numbered in week k? The value is 0 when the wildlife protection camera captures the image and 1 when the image is captured. This indicates that the image of the right side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual Is Zhou numbered? The value is 0 when the wildlife camera captures an image and 1 when the image is captured.

[0074] S4.4: Constructing the cost matrix based on the individual spatiotemporal matrix of the left side image of an individual Siberian tiger, the individual spatiotemporal matrix of the right side image of an individual Siberian tiger, and the relative distance matrix of the wildlife camera. The lower the cost, the greater the likelihood that the two are the same Siberian tiger individual.

[0075] Specifically, the spatiotemporal matrices of the left and right body images of individual Siberian tigers are traversed along the time dimension. At weekly intervals, the distance cost sets of both left and right Siberian tigers that were simultaneously photographed within all time periods are calculated. Its mathematical expression is as follows:

[0076]

[0077] in, This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The distance cost of an individual Siberian tiger This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual Zhou was numbered The wildlife conservation camera captured the image. This indicates that the image of the right side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual Zhou was numbered The wildlife conservation camera captured the image. Indicates the number is Wildlife camera and number The distance of the wildlife camera; This indicates the number of weeks the wildlife camera has been in operation.

[0078] The cost matrix is ​​calculated based on the distance cost set, and its mathematical expression is as follows:

[0079]

[0080] in, This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The matching cost of individual Siberian tigers; This represents the distance between the two furthest wildlife cameras. If individual Siberian tiger i on the left and individual Siberian tiger j on the right were present in the same week, then... = If individual Siberian tiger i on the left and individual Siberian tiger j on the right did not appear in the same week, then = .

[0081] Constructing an extended cost matrix , Its mathematical expression is as follows:

[0082]

[0083] Among them, if ,but In other cases, .

[0084] S4.5: Solve the extended cost matrix using the Hungarian matching algorithm;

[0085] Based on the output of the Hungarian matching algorithm, for each matching pair ,if and Then, the image database containing the left side image of the Siberian tiger will be numbered as follows: The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The Siberian tiger individuals are associated with the same Siberian tiger individual; if and This indicates that the image number in the database for the left side of the Siberian tiger is [number missing]. No corresponding image number for the Siberian tiger individual was found in the Siberian tiger right side body image database. Individual Siberian tigers; if and This indicates that the image number in the database for the right side of the Siberian tiger is [number missing]. No corresponding image number for the Siberian tiger individual was found in the Siberian tiger left side body image database. An individual Siberian tiger.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. However, these modifications 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 the present invention.

Claims

1. A method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information, characterized in that, Includes the following steps: Step S1: Collect and label data on individual Siberian tigers captured by wildlife cameras; Step S2: Predict key point information and body orientation of an individual Siberian tiger based on a key point detection algorithm; Step S3: Estimate individual information of Siberian tigers based on individual identification algorithms; Step S4: Combine spatiotemporal information to associate Siberian tiger individuals in the left and right side image databases; S4.1: Associate the captured images of individual Siberian tigers with the labeled individual ID information, as well as the statistical wildlife camera IDs and the time information of when they were captured, to construct a Siberian tiger spatiotemporal information database; S4.2: Obtain the latitude and longitude information of the wildlife camera based on the positioning module inside the wildlife camera, and calculate the relative distance matrix of the wildlife camera based on the latitude and longitude information. , For the number of wildlife conservation cameras, Indicates the number is Wildlife camera and number The distance of the wildlife camera; S4.3: Constructing an individual spatiotemporal matrix of the left side body image of an individual Siberian tiger based on the Siberian tiger spatiotemporal information database. Individual spatiotemporal matrix of the right side body image of an individual Siberian tiger , This represents the number of Siberian tiger individuals in the database containing images of the left side of their bodies. This represents the number of Siberian tiger individuals in the database containing images of the right side of their bodies. The number of weeks the wildlife camera worked. This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. Was the Siberian tiger individual numbered in week k? The value is 0 when the wildlife protection camera captures the image and 1 when the image is captured. This indicates that the image of the right side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual in Is Zhou numbered? The value is 0 when the wildlife protection camera captures the image and 1 when the image is captured. S4.4: Constructing the cost matrix based on the individual spatiotemporal matrix of the left side image of an individual Siberian tiger, the individual spatiotemporal matrix of the right side image of an individual Siberian tiger, and the relative distance matrix of the wildlife camera. ; Construct an extended cost matrix; S4.5: Solve the extended cost matrix using the Hungarian matching algorithm.

2. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 1, characterized in that, The specific implementation process of step S1 is as follows: S1.1: Collect images of individual Siberian tigers captured by wildlife conservation cameras; S1.2: Label the location and key information of individual Siberian tigers in the images, and assign individual numbers; S1.3: Collect information on the time when individual images of Siberian tigers were captured and the corresponding wildlife protection camera numbers; S1.4: Collect latitude, longitude, and attitude information of the installation location of the field protection camera.

3. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 1, characterized in that, The specific implementation process of step S2 is as follows: S2.1: Construct a Siberian tiger keypoint detection dataset based on the location and keypoint information of labeled Siberian tiger individuals; S2.2: Based on the keypoint detector, the Siberian tiger keypoint detection dataset is used to predict the side view image of an individual Siberian tiger, and the location and keypoint information of the individual Siberian tiger are obtained. S2.3: Construct a database of images of the left side of a Siberian tiger and an image database of images of the right side of a Siberian tiger based on the predicted images of the tiger's lateral side.

4. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 3, characterized in that, The location information of the marked Siberian tiger individual is the information of the bounding box containing the area of ​​the Siberian tiger individual.

5. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 3, characterized in that, There are six key points for the marked Siberian tiger individual: key point 1 is the connection between the head and body of the Siberian tiger individual; key point 2 is the connection between the rump and tail of the Siberian tiger individual. Key points 3 to 6 are the connection points between the left forelimb, right forelimb, left hindlimb, and right hindlimb of the Siberian tiger individual and its body.

6. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 5, characterized in that, The orientation of the Siberian tiger in the side view image is determined by comparing the x-coordinates of key point 1 and key point 2. If key point 1 is to the left of key point 2, the side view image of the Siberian tiger is the left side view image of the Siberian tiger; if key point 1 is to the right of key point 2, the side view image of the Siberian tiger is the right side view image of the Siberian tiger.

7. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 1, characterized in that, The specific implementation process of step S3 is as follows: S3.1: Based on the location and key point information of the labeled Siberian tiger individuals, the torso region image is extracted from the left side body image of the Siberian tiger individual, and the torso region image is associated with the labeled individual number; S3.2: After horizontally flipping the right-side body image in the image database of the right side of the Siberian tiger individual, merge it with the left-side body image in the image database of the left side of the Siberian tiger individual. The images in the merged image database are used as the training dataset for Siberian tiger individual recognition. S3.3: Construct an individual recognition network model and extract features from the images of the Siberian tiger's lateral trunk region on the Siberian tiger individual recognition training dataset, and then use the classification module to predict the individual information of the Siberian tiger.

8. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 1, characterized in that, The specific implementation process of step S4.4 is as follows: By iterating through the spatiotemporal matrices of images of the left and right sides of an individual Siberian tiger over time, and at weekly intervals, the distance cost sets of all Siberian tigers simultaneously photographed within any given time period are calculated. Its mathematical expression is as follows: ； in, This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The distance cost of an individual Siberian tiger This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual in Zhou was numbered The wildlife conservation camera captured the image. This indicates that the image of the right side of an individual Siberian tiger, numbered [number missing], is located in the image database. The first Siberian tiger individual in Zhou was numbered The wildlife conservation camera captured the image. Indicates the number is Wildlife camera and number The distance of the wildlife camera; The cost matrix is ​​calculated based on the distance cost set, and its mathematical expression is as follows: ； in, This indicates that the image of the left side of an individual Siberian tiger, numbered [number missing], is located in the image database. The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The matching cost of individual Siberian tigers; This indicates the distance between the two farthest wildlife cameras; Constructing an extended cost matrix , Its mathematical expression is as follows: 。 9. The method for associating left and right side images of an individual Siberian tiger based on spatiotemporal information according to claim 8, characterized in that, The specific implementation process of step S4.5 is as follows: Based on the output of the Hungarian matching algorithm, for each matching pair ,if and Then, the image database containing the left side image of the Siberian tiger will be numbered as follows: The image of the Siberian tiger individual and the right side of the Siberian tiger individual is numbered in the image database. The Siberian tiger individuals are associated with the same Siberian tiger individual; if and This indicates that the image number in the database for the left side of the Siberian tiger is [number missing]. No corresponding image number for the Siberian tiger individual was found in the Siberian tiger right side body image database. Individual Siberian tigers; if and This indicates that the image number in the database for the right side of the Siberian tiger is [number missing]. No corresponding image number for the Siberian tiger individual was found in the Siberian tiger left side body image database. An individual Siberian tiger.

Images