Articulation backcast shooting intent pattern recognition method

Abstract

The present application relates to the technical field of pattern classification, in particular to a joint motion backstepping shooting intention pattern recognition method, comprising the following steps: collecting joint node trajectories, extracting cooperative path features, fitting reference path lines, and identifying sample time sequence changes to determine shooting intention. In the present application, the spatial structure of the forearm vector is constructed in the continuous frame and the direction continuity criterion is established to accurately extract the main path information in the forearm rotation trajectory. In combination with the angle consistency judgment of the shoulder, elbow and wrist path, the spatial combination of multiple paths is constructed. After establishing the skeleton offset reference standard relying on the linear fitting method of node trajectory, the consistency performance of subsequent samples in spatial offset and direction projection can be quantified, the accurate discrimination ability of path trend consistency and time sequence evolution relationship can be improved, the recognition stability and judgment reliability of shooting intention can be enhanced, the motion interference caused by posture change can be effectively overcome, and the extraction accuracy of complex motion intention can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pattern classification, in particular to a joint motion reverse push shot intention pattern recognition method. BACKGROUND

[0002] The technical field of pattern classification relates to the research on the identification, classification and judgment of data patterns by computers, including the establishment of mathematical models or the training of identification models to realize the automatic discrimination and classification of patterns in image, sound, action and other data, and the systematic identification and classification of data patterns through sample training, parameter setting and similarity measurement methods. Among them, the joint motion reverse push shot intention pattern recognition method refers to obtaining the dynamic motion information of a series of joints of a human body before shooting, and then judging whether the moving subject has a shooting intention. It is aimed at the pre-judgment of shooting behavior in sports intention recognition, usually adopts a time sequence analysis method based on key skeleton point trajectory sequence, and completes the recognition by constructing a multi-dimensional action template database and combining posture sequence classification rules according to the relative displacement relationship and speed change characteristics between different joints.

[0003] In the existing shooting intention recognition process, classification and judgment are mainly based on the relative displacement and speed change between joints. When the motion change amplitude is small or the path trend is not obvious, similar trajectory misjudgment or posture fragment matching error may occur. When the moving subject performs non-shooting action but the local posture is close, the system may produce deviation in intention recognition. The time sequence template library relied on lacks recognition ability in dealing with the multi-path differentiation trend in continuous motion, resulting in the problem of motion recognition confusion in diversified shooting preparation actions or false action situations, reducing the adaptation range and judgment accuracy in actual motion discrimination scenarios. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art, and a joint motion reverse push shot intention pattern recognition method is proposed.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a joint motion reverse push shot intention pattern recognition method, comprising the following steps:

[0006] S1: Obtain the spatial position sequence of the elbow and wrist nodes of the moving person in the continuous motion frame, construct a unit direction vector sequence, judge the angle change trend between each pair of adjacent vectors, select the vector continuous segment with continuous direction change, and determine the main path according to the spatial trend to obtain the forearm rotation trajectory structure;

[0007] S2: According to the main path direction vector in the forearm rotation track structure, the direction included angle of the shoulder to elbow path and the elbow to wrist path is judged, the path segments with consistent direction and consistent trend are screened, the continuity information between the path segments is integrated, and a joint path cooperative segment is obtained;

[0008] S3: According to the shoulder, elbow and wrist node position sequence in the joint path cooperative segment, the spatial coordinate point set corresponding to the continuous time sequence of each node is extracted, a reference path line penetrating the path set is constructed, the spatial offset of each node to the reference path line is calculated, and skeleton path fitting reference data is obtained;

[0009] S4: According to the skeleton path fitting reference data, the point set of the subsequent node path sample is judged by spatial offset and direction projection, the path segment with consistent direction with the reference path line is screened, and the path segment is uniformly marked to obtain a sample path intention set.

[0010] As a further scheme of the application, the forearm rotation track structure includes main path trend, direction change trend record and vector continuous segment number, the joint path cooperative segment includes path included angle relationship, spatial trend consistency and path segment connection feature, the skeleton path fitting reference data includes node spatial offset, fitting path line direction and error distribution characteristic, and the sample path intention set includes path direction projection characteristic, spatial offset mode and label category information.

[0011] As a further scheme of the application, the specific acquisition steps of the forearm rotation track structure are:

[0012] S111: Obtain the spatial position sequence of the elbow and wrist nodes in the continuous motion frame, and obtain the unit direction vector sequence according to the change of the spatial position between adjacent frames;

[0013] S112: According to the unit direction vector sequence, the included angle change trend between any two adjacent vectors is judged, the vector segment with continuous direction change is screened, and a vector continuous segment is obtained;

[0014] S113: According to the vector continuous segment, the spatial trend information of each segment is extracted, the spatial trend sequence of all segments is integrated, and a forearm rotation track structure is obtained.

[0015] As a further scheme of the application, the specific acquisition steps of the joint path cooperative segment are:

[0016] S211: Obtain the direction vectors of each continuous segment in the shoulder-to-elbow path and the elbow-to-wrist path according to the main path direction vector in the forearm rotation trajectory structure, extract the direction features of each path segment, and perform direction consistency judgment with the main path direction vector, identify the path segments that remain consistent in direction by comparing whether their spatial trends are within the allowed deviation range, and generate a path direction judgment result;

[0017] S212: Based on the path direction judgment result, match the segments that are consistent in direction in the shoulder-to-elbow path segment and the elbow-to-wrist path segment, identify the combination relationship between the two path segments that are continuous in time and consistent in direction based on the frame number and time sequence information of the start and end nodes of each segment, filter the segment sequences that have continuous trend association, and obtain path continuous segment information;

[0018] S213: According to the path continuous segment information, extract the connection state of each continuous segment in the spatial structure, arrange the distribution features in the motion sequence, establish the combination relationship between the shoulder-to-elbow path segment and the elbow-to-wrist path segment that is consistent in spatial direction and time trend, and summarize the combination state between each path segment pair through unified marking, to obtain joint path cooperative segments.

[0019] As a further scheme of the present application, the specific acquisition steps of the skeleton path fitting reference data are:

[0020] S311: According to the path sequence of the shoulder, elbow, and wrist nodes in the joint path cooperative segment, extract the spatial coordinate data under the corresponding time frame, record the three-dimensional position coordinates of the three nodes in each frame, and construct a node position set in time sequence form, group and arrange the data according to the node type and frame number, and generate node trajectory set data;

[0021] S312: Based on the node trajectory set data, select the connection line segments with continuity in all path segment sets as a path sample set, establish a straight line in space that passes through the entire node distribution as a fitting reference path according to the position coordinates of all node connection lines in the path sample set in three-dimensional space, and construct a spatial reference direction along the minimum direction deviation to obtain reference path line direction data;

[0022] S313: According to the reference path line direction data, extract the shortest spatial distance from each node frame position point to the path line, construct the offset vector sequence corresponding to each node, and judge the spatial offset trend of the node distribution point relative to the fitting path, calculate the distance value of each node to the fitting path in all frames, and integrate and establish the skeleton path fitting reference data.

[0023] As a further scheme of the present application, the specific acquisition steps of the sample path intention set are:

[0024] S411: According to the skeleton path fitting reference data, the spatial coordinate point set of the subsequent node path sample is obtained, the positions of the three nodes of shoulder, elbow and wrist are extracted in each frame, and a continuous path point sequence is formed in time sequence, the point cloud set of all nodes is matched with the fitting path reference data, a unified coordinate index structure is established, and a sample node point set structure is obtained;

[0025] S412: Based on the sample node point set structure, the direction vector of each path segment is extracted, and the direction consistency with the direction vector of the reference path line is judged, according to the preset direction consistency threshold, the path segment consistent with the direction of the reference path line is screened, the path segment meeting the condition is marked and recorded, and path direction consistent segment information is obtained.

[0026] S413: According to the path direction consistent segment information, all path segments meeting the condition are uniformly marked and processed, the frame number range, start and end position and direction attribute of each path segment are integrated, a path segment identification system is established, a collection structure which can be used for subsequent trajectory classification and judgment is formed, and a sample path intention collection is obtained.

[0027] As a further scheme of the application, the method further comprises:

[0028] S5: According to the node arrangement order of each path in the sample path intention collection, the time sequence structure of the shoulder-elbow and elbow-wrist path segments is compared in turn, whether the node arrangement maintains a continuous unidirectional change trend is recorded, and the path meeting the time progression relationship is identified, so as to obtain a shooting intention recognition result.

[0029] The shooting intention recognition result comprises node arrangement order feature, path time sequence continuity record and motion trend judgment index.

[0030] As a further scheme of the application, the specific acquisition steps of the shooting intention recognition result are:

[0031] S511: According to the path identification in the sample path intention collection, the time sequence index of the shoulder, elbow and wrist nodes in each path segment is extracted, the node arrangement structure of the shoulder-elbow and elbow-wrist two path segments is obtained in turn according to the frame order, the three-dimensional coordinates of the nodes are corresponded with the time sequence, the change trend of each node in the continuous frame is identified, the adjacent node sequences of shoulder to elbow and elbow to wrist are compared, the nodes with increasing time index and continuous spatial displacement are screened, and a node time sequence matrix data is formed.

[0032] S512: Based on the node timing matrix data, for the shoulder-elbow and elbow-wrist two-path segments, based on the time sequence of each frame node position, the time progression relationship of the two-path segments is compared, whether the time sequence of adjacent nodes is consistent is checked, through comparing the time change mode of continuous frames, the path segment with stable change trend in time dimension is identified, the path segment meeting the time sequence requirement is marked and stored, and a time progression trend path set is obtained;

[0033] S513: According to the time progression trend path set, the node arrangement continuity of the shoulder-elbow and elbow-wrist path segments is verified, the path segments not maintaining the one-way change trend are screened out, only the path segments meeting the requirements of time sequence and spatial continuity are reserved, and the path segment sequence is integrated to generate a shooting intention recognition result.

[0034] Compared with the prior art, the advantages and positive effects of the present application are that:

[0035] In the present application, by constructing the spatial structure of the forearm vector in continuous frames and establishing the direction continuity criterion, the main path information in the forearm rotation trajectory is accurately extracted, the angle consistency of the shoulder-elbow-wrist path is judged, the spatial combination of multiple paths is constructed, and after the linear fitting method of the node trajectory is established, the skeleton offset reference standard is established, the consistency performance of the subsequent sample in space offset and direction projection can be quantified, by comparing and identifying the time sequence characteristics of the path node sequence, the accurate discrimination ability of the path trend consistency and the time sequence evolution relationship can be improved, the recognition stability and judgment reliability of the shooting intention can be enhanced, the motion interference caused by the posture change can be effectively overcome, and the extraction accuracy of the complex motion intention can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The main step flowchart of the present application is shown in the figure;

[0037] Figure 2 The forearm rotation trajectory structure acquisition flowchart of the present application is shown in the figure;

[0038] Figure 3 The joint path cooperative segment acquisition flowchart of the present application is shown in the figure;

[0039] Figure 4 The skeleton path fitting reference data acquisition flowchart of the present application is shown in the figure;

[0040] Figure 5 The sample path intention set acquisition flowchart of the present application is shown in the figure;

[0041] Figure 6 The shooting intention recognition result acquisition flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0042] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0043] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0044] Please refer to Figure 1 , joint motion backcast shooting intention mode recognition method, comprising the following steps:

[0045] S1: Obtain the spatial position sequence of the elbow and wrist nodes of the moving person in the continuous motion frames, construct a unit direction vector sequence according to the spatial position change between adjacent frames, judge the angle change trend between each pair of adjacent vectors, screen the vector continuous section whose direction change remains continuous, determine the main path according to the spatial trend of the vector continuous section, and obtain the forearm rotation trajectory structure;

[0046] S2: According to the direction vector of the main path in the forearm rotation trajectory structure, judge the direction angle of the path from the shoulder to the elbow and the path from the elbow to the wrist, screen the path section with consistent direction and consistent trend, integrate the continuity information between the path sections, mark the spatial combination with consistent trend between the paths, and obtain the joint path coordination section;

[0047] S3: According to the position sequence of the shoulder, elbow and wrist nodes in the joint path coordination section, extract the spatial coordinate point set corresponding to the continuous time sequence of each node, establish the path set of the node connection section and construct the reference path line through the path set (use the least square method linear fitting to find the best fitting straight line by minimizing the sum of squares of errors), measure the spatial offset of each node to the reference path line, and obtain the skeleton path fitting reference data;

[0048] S4: According to the skeleton path fitting reference data, judge the spatial offset and direction projection of the point set of the subsequent node path sample, screen the path section with consistent direction with the reference path line, and uniformly mark the path section to obtain the sample path intention set;

[0049] S5: According to the node arrangement order of each path in the sample path intention set, the time sequence structure of the shoulder-elbow and elbow-wrist path segments is compared in turn, whether the node arrangement maintains a continuous unidirectional change trend is recorded, and the paths that meet the time progression relationship are identified to obtain the shooting intention recognition result.

[0050] The forearm rotation trajectory structure includes the main path direction, the direction change trend record, and the vector continuous segment number, the joint path coordination segment includes the path angle relationship, the space direction consistency, and the path segment connection feature, the skeleton path fitting reference data includes the node space offset, the fitting path line direction, and the error distribution characteristic, the sample path intention set includes the path direction projection characteristic, the space offset mode, and the label category information, and the shooting intention recognition result includes the node arrangement order feature, the path time sequence continuity record, and the action trend judgment index.

[0051] Please refer to Figure 2 , the specific steps of S1 are as follows:

[0052] S111: Obtain the space position sequence of the elbow and wrist nodes in the continuous motion frame, and obtain the unit direction vector sequence according to the space position change between adjacent frames;

[0053] In the process of obtaining the space position sequence of the elbow and wrist nodes in the continuous motion frame, first, the high-speed camera device is used to continuously capture 120 frames of image data per second. After each frame of image is calibrated, the three-dimensional coordinate point information of the elbow and wrist is extracted. In the example, the position of the elbow in the first frame is (120.2mm, 85.6mm, 30.1mm), and the position of the wrist in the same frame is (230.7mm, 90.4mm, 28.8mm). In the second frame, they are (121.0mm, 86.1mm, 30.5mm) and (232.0mm, 91.0mm, 29.3mm), respectively. The node positions of the two frames are stored in array form for subsequent processing. At the same time, the inter-frame space displacement difference is calculated to obtain the displacement vectors of the elbow and wrist between different frames. The displacement vector is obtained from the node coordinate difference. For example, the three-dimensional displacement of the wrist from the first frame to the second frame is (1.3mm, 0.6mm, 0.5mm). In order to unify the space direction features between subsequent frames, the displacement vectors of each adjacent frame are normalized to unit vectors. The length of the unit vector is standardized to 1 for subsequent analysis of the space direction. For example, the displacement vector of the wrist from the first to the second frame (1.3, 0.6, 0.5) is normalized to the corresponding unit direction vector (0.857, 0.395, 0.329). The same operation is applied to all nodes in the subsequent frame sequence. In 20 frames of data, the unit direction vector set between each frame can be obtained in turn and form a sequence. Finally, the unit direction vector sequence is obtained.

[0054] S112: According to the sequence of unit direction vectors, the change trend of the included angle between any two adjacent vectors is judged, the vector segment with continuous direction change is screened, and the vector continuous segment is obtained;

[0055] According to the sequence of unit direction vectors, the continuity of the direction change between adjacent frames needs to be analyzed frame by frame. First, the included angle between adjacent vectors is calculated. The cosine value of the included angle can be obtained by the ratio of the dot product and the length of the vector. Then, the angle is converted to the angle system by the inverse cosine function. For example, the dot product of the wrist direction vector of the first frame and the second frame and the direction vector from the second frame to the third frame is 0.92, and the corresponding included angle is 23.1°. Based on this, a sequence of included angle changes is established to analyze the direction smoothness. The vector segment with a continuous included angle change less than 30° is classified into the same paragraph. 30° is taken as the direction continuity threshold value, which is derived based on the experimental data of the upper limb movement of the human body. According to the actual test in the movement experiment of 10 subjects, the statistical results show that the continuous included angle is mostly distributed between 10° and 28° under natural rotation, as shown in Table 1.

[0056] Table 1: Vector included angle experiment statistical table

[0057]

[0058] As shown in Table 1, when the included angle change exceeds 30°, it is considered as a direction mutation and is no longer classified into the same continuous vector segment. Therefore, 3 vector continuous segments satisfying the continuity condition are obtained in the 20 frame experimental data.

[0059] S113: According to the vector continuous segment, the spatial trend information of each segment is extracted, the spatial trend sequence of all segments is integrated, and the forearm rotation trajectory structure is obtained;

[0060] According to the vector continuous segment, the spatial trend information of each segment needs to be extracted and the spatial sequence needs to be established for integration. First, the overall direction of each vector set in the three-dimensional space is determined. The average direction coordinates of all unit direction vectors of each segment are calculated to form a three-dimensional trend vector representing the main direction of each segment. For example, the average components of all unit direction vectors of the first vector continuous segment (frame 1 to frame 6) in X, Y and Z directions are (0.86, 0.42, 0.30), and the average components of all unit direction vectors of the second vector continuous segment (frame 7 to frame 12) in X, Y and Z directions are (0.82, 0.35, 0.46). Similarly, the average components of all unit direction vectors of the third vector continuous segment (frame 13 to frame 18) in X, Y and Z directions are (0.77, 0.50, 0.39). Arranging these three spatial trend vectors in sequence according to the frame sequence can obtain the spatial trend sequence. Then, combining the frame sequence of the continuous segment and the time stamp of each segment, the complete trajectory of the forearm rotation in the three-dimensional space is finally reconstructed, and the forearm rotation trajectory structure is obtained.

[0061] Please refer to Figure 3 , the specific steps of S2 are:

[0062] S211: According to the main path direction vector in the forearm rotation trajectory structure, the direction vectors of each continuous segment in the shoulder-to-elbow path and the elbow-to-wrist path are obtained respectively, the direction features of each path segment are extracted, and the direction consistency is judged with the main path direction vector. The path segments that remain consistent in direction are identified by comparing whether their spatial trends are within the allowed deviation range, and the path direction judgment result is generated;

[0063] Based on the main path direction vector in the forearm rotation trajectory structure, the spatial direction data of each continuous segment in the shoulder-to-elbow path and the elbow-to-wrist path needs to be extracted first. In this process, the path is disassembled by frame identification and spatial node coordinate recording. Each path segment is constructed with the direction vector of the starting frame and the ending frame. For example, the direction vector of the 2nd to 4th frame segment in the shoulder-to-elbow path can be obtained by the difference between the 2nd frame position (110.2 mm, 75.6 mm, 34.2 mm) and the 4th frame position (115.4 mm, 80.3 mm, 36.1 mm) of the elbow. After unitization processing of the direction vector, the spatial angle is judged with the forearm main path direction vector. The direction consistency is judged by whether the included angle is lower than the threshold value of 15°. The threshold value is derived from the average stable rotation angle experiment of 12 subjects. The angle deviation is calculated by the inverse cosine of the ratio of the vector dot product and the module length. For example, if the direction vector of a path segment is (0.86, 0.44, 0.24) and the main path direction is (0.89, 0.42, 0.21), the dot product is 1.0006, and the included angle is 9.2°, which meets the direction consistency condition. The threshold value of 15° is the average angle deviation tolerance value set by repeated experiments. The data is shown in Table 2.

[0064] Table 2 Path direction consistency judgment threshold setting table

[0065]

[0066] As shown in Table 2, the included angle between the shoulder-elbow segment and the main path of 10 out of the 12 subjects in the experiment is less than 15°. Therefore, this angle is used as the basis for direction consistency judgment. After further identifying multiple path segments that meet the conditions, the path direction judgment result is obtained.

[0067] S212: Based on the path direction judgment result, the segments with consistent directions in the shoulder-to-elbow path and the elbow-to-wrist path are matched. Based on the frame number and time sequence information of the start and end nodes of each segment, the combination relationship between the two path segments that are continuous in time and consistent in direction is identified. The segment sequence with continuous trend association is screened, and the path continuous segment information is obtained.

[0068] After obtaining the path direction judgment result, the path segments meeting the direction consistency condition are arranged in two directions of shoulder to elbow and elbow to wrist respectively, and whether they constitute a continuous time segment is judged by comparing the time frame number sequences of adjacent path segments. The specific steps are as follows: the frame numbers of the shoulder-elbow path direction consistent segment are extracted, such as frame numbers 5 to 7, there is a segment corresponding to frame numbers 6 to 8 in the elbow-wrist path, which means that the two form a continuous overlapping area in the frame time, and this is taken as a continuous segment candidate combination. For the path segment pair that partially exists frame jump but the directions are consistent, the maximum frame jump value is set to not more than 2 frames as the allowed window according to the actual delay range. If the elbow-wrist path segment is frame 9 to 11, and the shoulder-elbow segment is frame 6 to 8, the time difference is 1 frame, which can still be regarded as a continuous segment. The judgment basis is based on the allowed frame rate error range of 0.01 seconds to 0.02 seconds in the dynamic sampling process. This time window is derived from the experimental data statistics of different equipment frame rate jitter control results, and then the path segments that constitute a reasonable continuation in time are retained and marked as combined valid paths. According to the number of continuously appearing valid path combinations, the combinations that constitute a stable continuous sequence in time frames are extracted, for example, there are direction consistent path combination segments in the 10th frame to the 16th frame in the motion process of a certain subject for 7 frames, which can be included in the continuous segment information structure, and the path continuous segment information is obtained.

[0069] S213: According to the path continuous segment information, the connection state of each continuous segment in the spatial structure is extracted, the distribution characteristics in the motion sequence are arranged, the combination relationship between the shoulder to elbow path segment and the elbow to wrist path segment which are consistent in spatial direction and time trend is established, and the combination state between each path segment pair is summarized through unified marking method, and the joint path coordination segment is obtained.

[0070] According to the path continuous segment information, the connection relationship between each continuous segment in the spatial structure needs to be further combed. The starting node, ending node and spatial coordinates between the path segments are extracted, and the combination is arranged through the direction relationship and time sequence. If there are multiple path segments that are consistent in spatial direction and have overlapping relationship between the starting and ending nodes, for example, path segment A starts at frame 5 and ends at frame 8, path segment B starts at frame 7 and ends at frame 10, there are time frame intersection frames 7 and 8, the direction vector deviation is less than 10°, then they are merged into the same combination structure. In the combination structure construction process, the paragraph number, spatial trend information and frame time distribution are recorded, the combination identification of the segment pair structure is formed, each structure corresponds to a type of spatial combination label, and the label value is set according to the continuity of the spatial starting and ending points, the time sequence consistency and the direction matching score. The direction matching is taken as the high matching standard that the experimental standard angle deviation is less than 15° and the spatial deviation is within 5 mm. Finally, all the combination relationship structures are numbered, and the structures meeting the consistency marking standard are included in the coordination marking set to establish the joint path coordination segment.

[0071] Please refer toFigure 4 The specific steps of S3 are:

[0072] S311: According to the path sequence of the shoulder, elbow, and wrist nodes in the joint path coordination segment, the spatial coordinate data in the corresponding time frame is extracted, the three-dimensional position coordinates of the three nodes are recorded in each frame, and the node position set is constructed in time sequence form. The data is grouped and arranged according to the node type and frame number, and the node trajectory set data is generated;

[0073] According to the position sequence of the shoulder, elbow, and wrist nodes in the joint path coordination segment, the three-dimensional coordinate information corresponding to each node in the continuous frame sequence is first extracted. The shoulder node is represented by infrared marker point number P1, the elbow node is P2, and the wrist node is P3. The spatial position of each marker point in each frame is a three-dimensional coordinate set. For example, in the first frame, the P1 coordinates are (112.4, 64.8, 30.3), the P2 coordinates are (125.0, 71.2, 34.1), and the P3 coordinates are (138.6, 76.9, 36.5). This process extracts 300 frames of data throughout the measurement period. The frame number and corresponding coordinates of the nodes are recorded by index number and form a node-frame mapping matrix. Then, a time sequence coordinate table is constructed. The coordinates of the same node in adjacent frames are connected in sequence to form a set of path segments for each node. Each group of paths consists of multiple point pairs. For example, the path segment of the P1 node can be generated by the space segment (frame 1)-(frame 2), (frame 2)-(frame 3), etc. The three-dimensional vector direction of such a segment can be directly calculated from the coordinate difference between adjacent frames. The sampling time between frames is set to 0.0083 seconds, i.e., a sampling rate of 120 Hz. 120 coordinate points are recorded per second, forming a set of node line segments of about 890, and constructing a path structure table with start and end frame indices, node type identifiers, and three-dimensional spatial position data, as shown in Table 3.

[0074] Table 3 Node Path Structure Table

[0075]

[0076] As shown in Table 3, by summarizing the node position pairs of all frames, the node trajectory set data that can be used for subsequent fitting has been generated.

[0077] S312: Based on the node trajectory set data, select the connected line segments with continuity in all path segment sets as the path sample set. According to the position coordinates of all node connections in the path sample set in the three-dimensional space, establish a straight line that passes through the entire node distribution as a fitting reference path, and construct a spatial reference direction along the minimum direction deviation to obtain reference path line direction data;

[0078] After calling the node trajectory set data, a representative path segment needs to be selected for spatial structure fitting. The midpoint of each node connecting segment is extracted as a fitting reference sample point. The midpoint of the shoulder-elbow connecting segment is calculated as the average of the coordinates of the two ends. For example, P1 in the 20th frame is (120.0, 70.5, 31.8) and P2 is (130.4, 75.3, 35.2), and the midpoint is (125.2, 72.9, 33.5). The midpoint of the elbow-wrist connecting segment is obtained in the same way, which is (135.5, 78.4, 36.3). A total of about 580 sets of midpoint data are obtained in all frames. All the midpoints are arranged in time sequence to form a point cloud set for identifying the distribution trend in three-dimensional space. Then, a straight line is drawn through all the sample points based on the midpoint coordinate set to represent the main direction of the overall node activity. This direction line needs to satisfy the principle of maximizing the fitting direction of the point cloud trend. Therefore, the principal component analysis (PCA) method is used to process the point cloud set composed of all the midpoints. The direction of the first principal component is taken as the direction vector of the reference path line. Finally, two key parameters, the starting point of the direction line and the direction vector, are generated to calculate the offset for subsequent offset calculation to obtain the reference path line direction data.

[0079] S313: According to the reference path line direction data, the shortest spatial distance of each node interframe position point to the path line is extracted, the corresponding offset vector sequence of each node is constructed, and the spatial offset trend of the node distribution point relative to the fitted path is judged. The formula is:

[0080] ;

[0081] The distance values of all frames of each node to the fitted path are obtained by calculation, and the skeleton path fitting reference data is established by integration. Wherein, represents the offset value of the node in the frame, is the spatial coordinate of the node in the frame, and are the starting and ending point coordinates of the reference path line at the position of the frame, is the length of the path line on the plane;

[0082] According to the reference path line direction data, the spatial position of each node in each frame needs to be extracted, and the minimum spatial offset relative to the reference path line is determined. First, the path position of the reference path line in the current frame is identified. By setting the spatial parameter equation of the path line, the perpendicular projection point of the node to the straight line is established at the corresponding time point of the current frame node. The foot point is taken as the fitting reference. Then, the spatial offset vector is constructed from the coordinate difference between the node three-dimensional coordinate and the foot point on the reference line. The Euclidean distance between them is calculated as the offset distance value. For example, in the 28th frame, the actual spatial coordinate of the shoulder node P1 is , and the position of the corresponding point on the reference path line is the starting point , the direction vector is set as , the space straight line equation is constructed in this direction, and the current node point position is substituted into the distance formula from the space point to the straight line to calculate the offset value. Substitute each parameter into the formula:

[0083] , , ;

[0084] , , ;

[0085] , The direction unit vector is 1.0:0.5;

[0086] Direction component difference: , ;

[0087] It is calculated by referring to the parameter equation of the path line. If the starting point of the path line is (xo, yo, Zo) and the direction vector is (a, b, c), then the coordinates of a point on the line are (xo+at, yo+bt, zo+ct). By projecting the node (xi, yi) onto the XY plane of the straight line, the t value can be obtained, that is, z(ti)=Zo+ct. The Z direction reference value is 31.5, which is adjusted with the linear change of the node in the main direction path length, and is strictly coupled with the slope of the direction vector, to reflect the fitting positioning of the height position.

[0088] Then the distance item of the plane part is:

[0089] ;

[0090] The height direction offset item is:

[0091] ;

[0092] The final offset value is:

[0093] ;

[0094] The node has a spatial offset value of 2.324 mm in the frame, which belongs to the range of offset values exceeding the reference threshold, and the offset value reference interval is set to [0 mm, 2.0 mm]. The setting is based on the statistical results of the node position offset distribution interval of the subjects in the shoulder-elbow-wrist coordinated motion in the natural barrier-free state in multiple experiments. Specifically, 9 subjects, 30 frames of motion were covered. The node offset value was measured by the Euclidean distance, and the offset value of each frame was accumulated and counted according to the node category. The average offset value is concentrated between 1.2 mm and 1.8 mm, and the standard deviation is less than 0.5 mm. Therefore, 2.0 mm is set as the upper limit of the normal fluctuation interval. This value may slightly expand when the node motion speed rises or the direction mutation frequency increases, but it remains stable within the standard step frequency range (0.6 Hz to 1.2 Hz).

[0095] During the entire measurement process, the offset of all frames and three nodes was processed frame by frame, and the offset value list of each frame of the node was obtained. The maximum offset, minimum offset and mean value of the node in all frames were also counted. If the offset direction (such as the X direction) of a node continuously increases or continuously decreases in more than 5 consecutive frames, it is marked as an offset trend continuation area. The trend is judged by the consistent section of the sign sequence of the offset value, and the frame segment length is counted to mark the offset directionality. In addition, the outlier points of the offset value of all frames are determined. If the offset value exceeds 4.5 mm, it is marked as an outlier in the statistics. If the proportion of all outlier data is higher than 10% of the total number of points, it is warned as an abnormal trajectory segment. The offset data statistical results will be used for motion evaluation reference, and the skeleton path fitting reference data will be finally established.

[0096] In the formula, the first part represents the node position The two-dimensional vector cross product absolute value of the start point of the reference path line and the direction vector The geometric meaning is the vertical distance from the node to the path line in the plane multiplied by the length of the path line. By dividing by , the length of the path line is normalized, so that this part of the result directly represents the minimum offset distance of the node to the path line in the XY plane. The second part reflects the height offset of the node in the Z direction. The calculation method is the absolute value of the difference between the node Z coordinate and the estimated height of the reference path line at the corresponding position of the frame , which is used to ensure that the offset value is positive regardless of whether the node is above or below the path line. The denominator is 1 to maintain dimensional consistency, which is convenient for subsequent direct addition of the plane offset.

[0097] Finally, by directly adding the plane offset and the height offset, the comprehensive spatial offset value of the node relative to the reference path line is obtained.

[0098] Referring to Figure 5 , the specific steps of S4 are:

[0099] S411: According to the fitting reference data of the skeleton path, the spatial coordinate point set of the subsequent node path sample is obtained, the positions of the shoulder, elbow and wrist nodes are extracted in each frame, and a continuous path point sequence is formed in time sequence, the point cloud set of all nodes is matched with the fitting path reference data, a unified coordinate index structure is established, and the sample node point set structure is obtained;

[0100] According to the fitting reference data of the skeleton path, the spatial coordinate point set of the subsequent node path sample is obtained, in this process, the three-dimensional coordinates of the shoulder, elbow and wrist nodes are extracted from each frame, and they are arranged in time sequence to form a complete path point sequence through the frame number, after all the path sample points are unified and arranged, an index structure containing nodes, frame numbers, spatial coordinates and corresponding time stamps is constructed, on this basis, each node sample point is matched with the reference path, the three-dimensional coordinate positions of all points are corrected for position deviation, to ensure the accuracy of the data, for example, if the Euclidean distance between the node coordinates in a frame and the corresponding point of the fitted reference path is greater than 2.0mm, it is marked as an offset abnormal point and excluded in the subsequent calculation, through this method, a quality consistent path sample point set is formed, and finally a classified, time continuous and traceable point cloud data structure is generated, which is used for subsequent path judgment and screening, to obtain the sample node point set structure.

[0101] Table 4 Node sample point space coordinate table

[0102]

[0103] As shown in Table 4, the node sample points maintain continuity in time sequence, ensuring the formation of a complete three-dimensional path trajectory data structure, providing basic data support for subsequent direction judgment.

[0104] S412: Based on the sample node point set structure, the direction vector of each path segment is extracted, and the direction consistency with the direction vector of the reference path line is judged, according to the preset direction consistency threshold, the path segments consistent with the direction of the reference path line are selected, the path segments meeting the conditions are marked and recorded, and the path direction consistent segment information is obtained;

[0105] The sample node point set structure is called, the direction vector of each path segment is extracted, and consistency is judged according to the reference path line direction vector. First, the node coordinates of the continuous frames in each path segment are obtained, the spatial position difference between the frames is used to construct the direction vector set of the path segment, and then the reference path direction vector is compared segment by segment. In this process, the direction consistency threshold is set to be an included angle ≤ 15°. The threshold is determined by experimental statistics. For example, in the dynamic sample data of 12 subjects, the average direction deviation of the shoulder to wrist path segment is 11.2°, and the standard deviation is 2.6°. 15° can cover more than 90% of the normal path segments. The path segments with a direction deviation less than the threshold are classified into the effective path segment set, and the corresponding frame interval and direction data are recorded.

[0106] Table 5 Path segment direction consistency judgment table

[0107]

[0108] As shown in Table 5, the path segments highly consistent with the reference path direction can be screened out through direction consistency judgment, providing a basic condition for subsequent path segment labeling and integration, and obtaining path direction consistent segment information.

[0109] S413: According to the path direction consistent segment information, all path segments meeting the conditions are uniformly labeled and processed, the frame number range, start and end position and direction attribute of each path segment are integrated, a path segment identification system is established, a collection structure which can be used for subsequent trajectory classification and determination is formed, and a sample path intention collection is obtained;

[0110] According to the path direction consistent segment information, all path segments meeting the conditions are uniformly labeled and processed. First, the frame number range, node start and end position and direction attribute of each path segment are extracted and sorted according to time sequence. The path segment unique identification number is uniformly allocated, the identification number is bound with the path segment attribute, and written into the sample path segment attribute collection. Indexes are established in the data structure, which is convenient for subsequent path classification and tracking analysis, for example, D1 path segment is labeled as “intention T1”, D2 path segment is labeled as “intention T2”, and these identifiers are mapped to the path segment sequence. Finally, the intention collection covering all effective path segments is integrated and formed.

[0111] Table 6 Sample path intention labeling table

[0112]

[0113] As shown in Table 6, all path segments consistent with the reference path direction are uniformly labeled, and the final identification system can be directly used for sample action classification and path intention recognition, and a sample path intention collection is obtained.

[0114] Please refer to Figure 6 , the specific steps of S5 are:

[0115] S511: According to the path identification in the sample path intention set, the time sequence index of the shoulder, elbow, and wrist nodes in each path segment is extracted, the node arrangement structure of the shoulder-elbow and elbow-wrist two segments is obtained in turn according to the frame order, the node three-dimensional coordinates are corresponded with the time sequence, the change trend of each node in the continuous frames is identified, the adjacent node sequence from shoulder to elbow and from elbow to wrist is compared, the nodes with increasing time index and continuous spatial displacement are screened, and the node time sequence matrix data is formed.

[0116] According to the path identification in the sample path intention set, the three-dimensional coordinates of the shoulder, elbow, and wrist nodes of each path segment and the corresponding frame number are first extracted, the original timestamp data collected by the high-speed camera equipment is combined to establish a complete time sequence index structure, and the spatial position and time position of each frame node are accurately corresponded. In the shooting scene, it is assumed that data of 10 testers in continuous shooting experiments is collected, the sampling rate of each action is 120 fps, the total frame number is about 120 frames, the shoulder, elbow, and wrist node coordinates in the frame sequence are read to establish an initial node point cloud data set. Next, according to the spatial continuity requirement of adjacent nodes, the three-dimensional distance between the shoulder-elbow and elbow-wrist in the same frame is detected, if the distance between adjacent nodes exceeds the reference threshold, the frame is marked as an abnormal frame and is excluded, and the threshold is set based on statistical experimental data. Taking the spatial distance measurement of the shoulder-elbow and elbow-wrist segments of the 10 testers in shooting as an example, the inter-frame average displacement of the shoulder-elbow segment is between 8.2 mm and 13.6 mm, and the inter-frame average displacement of the elbow-wrist segment is between 10.1 mm and 17.4 mm, and the effective threshold range under normal circumstances is 5 mm to 15 mm for the shoulder-elbow segment and 8 mm to 18 mm for the elbow-wrist segment, as shown in Table 7. In a typical shooting sample, the x, y, and z coordinates of the shoulder node of frame numbers 1 to 10 are between (210, 1300, 1450) and (225, 1315, 1470), the elbow node is between (330, 1105, 980) and (340, 1120, 1000), and the wrist node is between (460, 980, 620) and (475, 1000, 640), and the distance change of the continuous frames is verified by spatial calculation to conform to the threshold range, the qualified nodes are retained, and a three-dimensional coordinate matrix is generated in time sequence to form node time sequence matrix data.

[0117] Table 7 Reference threshold table for node displacement of shooting sample

[0118]

[0119] As shown in Table 7, the threshold range is calculated from the measured sample, the abnormal frames are excluded to ensure the continuity and data validity of the time sequence matrix in subsequent analysis, and finally the node time sequence matrix data is obtained.

[0120] S512: Based on the node timing matrix data, the time progression relationship of the two path segments is compared based on the time sequence of the node position of each frame, the time order of the adjacent nodes is checked for consistency, and the path segment that meets the time order requirement is marked and stored by comparing the time change pattern of the continuous frames to identify the path segment with stable change trend in the time dimension, and the time progression trend path set is obtained;

[0121] The node timing matrix data is called, the timestamp sequence of each frame is extracted for the shoulder-elbow and elbow-wrist two path segments in turn, and each frame acquisition time is bound with the corresponding three-dimensional node coordinates to construct the path time sequence set. In the specific experiment, the sampling rate is 120 fps, and the frame acquisition interval is about 4.17 ms. For example, in a shooting sample, the continuous frame time sequence of the shoulder-elbow segment is 0.000s, 0.004s, 0.008s to 1.236s, and the elbow-wrist segment time sequence is 0.016s, 0.020s, 0.024s to 1.256s. By detecting whether the time increment between adjacent frames is greater than 0, it can be judged whether the time sequence meets the continuity requirement of one-way increasing; if there is a time rollback or sampling loss between adjacent frames, for example, the time difference between the 58th frame and the 59th frame of the elbow-wrist segment is -0.012s, it is marked as invalid and the frame is excluded. Then, the valid time intervals of the two path segments are compared, and it is ensured that the time interval of the shoulder-elbow segment is completely before or synchronized with the elbow-wrist segment. In a sample, the valid time interval of the shoulder-elbow segment is [0.000s, 1.236s], and the valid time interval of the elbow-wrist segment is [0.016s, 1.256s], and there is no time overlap between the two path segments, which meets the standard time evolution requirement of continuous shooting action. The time sequences of all valid path segments are uniformly marked and numbered to obtain the time progression trend path set for subsequent node trend analysis.

[0122] S513: According to the time progression trend path set, the node arrangement continuity of the shoulder-elbow and elbow-wrist path segments is verified, the path segments that do not maintain a one-way change trend are excluded, only the path segments that meet the requirements of time sequence and spatial continuity are retained, and the path identification in the original sample path intention set is correspondingly mapped, the valid path segment sequence is integrated, and the shooting intention recognition result is generated;

[0123] According to the time progression trend path set, further integrate all shoulder-elbow and elbow-wrist path segments meeting the time sequence and spatial continuity requirements, map the effective path segments back to the original sample path intention set, and establish a unified identifier for each path segment. In the specific operation, the start frame number, end frame number, start and end node three-dimensional coordinate range and corresponding time interval of each path segment are extracted, for example, in sample A, the start and end frames of the shoulder-elbow segment are 12 to 64 frames, the time range is [0.050s, 1.020s], and the node coordinate range is between (210, 1280, 1440) and (260, 1360, 1500); the start and end frames of the elbow-wrist segment are 20 to 74 frames, the time range is [0.080s, 1.200s], and the coordinate range is between (340, 1120, 990) and (380, 1190, 1050). After numbering all effective path segments, combine the original sample path identifier to establish a path-to-time sequence mapping table, and realize consistent alignment across path segments. After data integration is completed, all path segments that do not meet the time progression or spatial continuity requirements are removed, high-quality trajectory data during the shooting process is retained, and finally the shooting intention recognition result directly applicable to action mode recognition is obtained.

[0124] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still falls within the protection scope of the present application.

Claims

1. A method for identifying a motion-reversal shooting intention pattern, characterized in that, The method comprises the following steps: S1: obtaining a sequence of spatial positions of elbow and wrist nodes of a moving person in continuous motion frames, constructing a sequence of unit direction vectors, judging a change trend of an included angle between each pair of adjacent vectors, screening a vector continuous segment in which the direction change remains continuous, and determining a main path according to a spatial trend to obtain a forearm rotation trajectory structure; S2: judging a direction included angle of a shoulder-to-elbow path and an elbow-to-wrist path according to a direction vector of the main path in the forearm rotation trajectory structure, screening a path segment in which the direction is consistent and the trend is consistent, integrating continuity information between path segments, and obtaining a joint path cooperative segment; S3: extracting a set of spatial coordinates corresponding to a continuous time sequence of each node according to a sequence of positions of shoulder, elbow, and wrist nodes in the joint path cooperative segment, constructing a reference path line penetrating through the path set, measuring a spatial offset of each node to the reference path line, and obtaining skeleton path fitting reference data; S4: judging a spatial offset and a direction projection of a point set of a subsequent node path sample according to the skeleton path fitting reference data, screening a path segment in which the direction is consistent with the reference path line, uniformly marking the path segment, and obtaining a sample path intention set.

2. The method of pitch pattern recognition of claim 1, wherein, The forearm rotation trajectory structure comprises a main path trend, a direction change trend record, and a number of vector continuous segments, the joint path cooperative segment comprises a path included angle relationship, a spatial trend consistency, and a path segment connection feature, the skeleton path fitting reference data comprises a node spatial offset, a fitting path line direction, and an error distribution characteristic, and the sample path intention set comprises a path direction projection characteristic, a spatial offset mode, and marked category information.

3. The method of pitch pattern recognition of claim 1, wherein, The specific acquisition steps of the forearm rotation trajectory structure are as follows: S111: obtaining a sequence of spatial positions of elbow and wrist nodes in continuous motion frames, and obtaining a sequence of unit direction vectors according to a change in spatial positions between adjacent frames; S112: judging a change trend of an included angle between any two adjacent vectors according to the sequence of unit direction vectors, screening a vector segment in which the direction change remains continuous, and obtaining a vector continuous segment; S113: extracting spatial trend information of each segment according to the vector continuous segment, integrating a sequence of spatial trends of all segments, determining a main path from the sequence, and obtaining a forearm rotation trajectory structure.

4. The articulation reverse-pivot shot intention pattern recognition method of claim 1, wherein, The specific acquisition steps of the joint path cooperative segment are as follows: S211: obtaining direction vectors of each continuous segment in a shoulder-to-elbow path and an elbow-to-wrist path according to a direction vector of the main path in the forearm rotation trajectory structure, extracting a direction feature of each path segment, judging a direction consistency with the direction vector of the main path, identifying a path segment in which the direction remains consistent by comparing spatial trends of each continuous segment within an allowed deviation range, and generating a path direction judgment result; S212: matching a segment in which the direction is consistent in a shoulder-to-elbow path segment and an elbow-to-wrist path segment based on the path direction judgment result, identifying a combination relationship in which two path segments are continuous in time and consistent in direction based on frame number and time sequence information of start and end nodes of each segment, screening a segment sequence having a continuous trend association, and obtaining path continuous segment information; S213: According to the path segment information, the connection state of each continuous segment in the spatial structure is extracted, the distribution characteristics in the motion sequence are arranged, the combination relationship between the shoulder-to-elbow path segment and the elbow-to-wrist path segment is established, the combination state between each path segment pair is summarized through unified marking, and the joint path coordination segment is obtained.

5. The articulation reverse-pivot shot intention pattern recognition method of claim 1, wherein, The specific acquisition steps of the skeleton path fitting reference data are: S311: According to the path sequence of the shoulder, elbow and wrist nodes in the joint path coordination segment, the spatial coordinate data under the corresponding time frame is extracted, the three-dimensional position coordinates of the three nodes are recorded in each frame, and the node position set is constructed in time sequence form, the data is grouped and arranged according to the node type and frame number, and the node trajectory set data is generated; S312: Based on the node trajectory set data, select the connected line segment with continuity in all path segment sets as the path sample set, establish a straight line through the whole node distribution in the three-dimensional space as the fitting reference path according to the position coordinates of all node connecting lines in the path sample set, and construct a spatial reference direction along the minimum direction deviation to obtain the reference path line direction data; S313: According to the reference path line direction data, the shortest spatial distance from each node frame position point to the path line is extracted, the offset vector sequence corresponding to each node is constructed, and the spatial offset trend of the node distribution point relative to the fitting path is judged, the distance value of each node to the fitting path in all frames is calculated, and the skeleton path fitting reference data is established.

6. The articulation reverse-pivot shot intention pattern recognition method of claim 1, wherein, The specific acquisition steps of the sample path intention set are: S411: According to the skeleton path fitting reference data, the spatial coordinate point set of the subsequent node path sample is acquired, the positions of the three nodes of shoulder, elbow and wrist are extracted in each frame, and a continuous path point sequence is formed in time sequence, the point cloud set of all nodes is matched with the fitting path reference data, and a unified coordinate index structure is established to obtain the sample node point set structure; S412: Based on the sample node point set structure, the direction vector of each path segment is extracted, and the direction consistency with the direction vector of the reference path line is judged, according to the preset direction consistency threshold, the path segments consistent with the direction of the reference path line are selected, the path segments meeting the conditions are marked and recorded, and the path direction consistent segment information is obtained; S413: According to the path direction consistent segment information, all path segments meeting the conditions are uniformly marked and processed, the frame number range, start and end position and direction attribute of each path segment are integrated, the path segment identification system is established, and the sample path intention set is obtained.

7. The method of pitch pattern recognition of claim 1, wherein, The method further comprises: S5: According to the node arrangement order of each path in the sample path intention set, the time sequence structure of the shoulder-elbow and elbow-wrist path segments is compared in turn, whether the node arrangement maintains a continuous and single-direction change trend is recorded, and the path meeting the time progression relationship is identified to obtain the shooting intention recognition result; The shooting intention recognition result includes node arrangement order characteristics, path time sequence continuity record and motion trend judgment index.

8. The articulation reverse-pivot shot intent pattern recognition method of claim 7, wherein, The specific acquisition steps of the shooting intention recognition result are: S511: According to the path identification in the sample path intention set, the time sequence index of the shoulder, elbow and wrist nodes in each path segment is extracted, the node arrangement structure of the shoulder-elbow and elbow-wrist two segments is obtained in turn according to the frame order, the node three-dimensional coordinates are corresponded with the time sequence, the change trend of each node in the continuous frame is identified, the adjacent node sequence of the shoulder to the elbow and the elbow to the wrist is compared, the nodes with the time index increasing and the spatial displacement continuous are screened, and the node time sequence matrix data is formed; S512: Based on the node time sequence matrix data, for the shoulder-elbow and elbow-wrist two segments, the time progression relationship of the two segments is compared based on the time sequence of the node position of each frame, whether the time order of the adjacent nodes is consistent is checked, the time change mode of the continuous frames is compared, the path segment with the change rate in the time dimension remaining in the predetermined range is identified, the path segment meeting the time order requirement is marked and stored, and the time progression trend path set is obtained; S513: According to the time progression trend path set, the node arrangement continuity of the shoulder-elbow and elbow-wrist path segments is verified, the path segment not maintaining the one-way change trend is screened out, only the path segment meeting the requirements of the time sequence and the spatial continuity is reserved, the path segment sequence is integrated corresponding to the path identification in the original sample path intention set, and the shot intention recognition result is generated.

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