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An Occluded Target Tracking Method for Tennis Sports Video Based on an Attentional Mechanism to Maximize Overlap.

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Bibliographic Details
Title: An Occluded Target Tracking Method for Tennis Sports Video Based on an Attentional Mechanism to Maximize Overlap.
Authors: Chen, Yongming
Source: International Journal of High Speed Electronics & Systems; Dec2025, Vol. 34 Issue 4, p1-26, 26p
Subject Terms: SUPPORT vector machines, FEATURE extraction, VECTOR data, SPORTS films, MULTIPLE target tracking, TENNIS, TRACKING radar
Abstract: Aiming at the poor anti-occlusion ability of target feature extraction in the process of tennis motion video occlusion target tracking, and the matching problem between target feature map and multiple moving targets, a tennis motion video occlusion target tracking method based on attention mechanism to maximize overlap is proposed. VGG16 neural network is used to stack the features extracted from the convolution layer through a filter to obtain key candidate moving target features. The temporal and spatial attention mechanisms are used to calculate the weight of the extracted candidate moving target features to improve the anti-occlusion ability of moving target feature extraction. The weighted moving target features are input into the support vector machine classifier. Aiming at the defect that SVM can only deal with vector data, a structured support vector machine (SSVM) is proposed. Through the SSVM, the overlap rate between the candidate moving target features and the actual moving target is output. The estimated position of the current video sequence frame is obtained according to the maximum overlap. Sample the frame target in the video sequence, and realize the occlusion target tracking in tennis motion video by cycling the above process. The experimental results show that the accuracy of feature extraction of this method is about 90%. After adding the weight distribution mechanism, the convergence speed is fast, and the mean square error is about 0.2, indicating that the method has a high accuracy of feature extraction. In the detection of overlap rate, the highest overlap rate is 0.912, indicating that the method has a high accuracy of overlap rate. In addition, in the tennis motion video occlusion target tracking and detection, it can accurately locate the target position, and surround all parts of the target. There will be no redundant blank in the target box, which fully reflects the accuracy and robustness of tennis motion video occlusion target tracking. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Aiming at the poor anti-occlusion ability of target feature extraction in the process of tennis motion video occlusion target tracking, and the matching problem between target feature map and multiple moving targets, a tennis motion video occlusion target tracking method based on attention mechanism to maximize overlap is proposed. VGG16 neural network is used to stack the features extracted from the convolution layer through a filter to obtain key candidate moving target features. The temporal and spatial attention mechanisms are used to calculate the weight of the extracted candidate moving target features to improve the anti-occlusion ability of moving target feature extraction. The weighted moving target features are input into the support vector machine classifier. Aiming at the defect that SVM can only deal with vector data, a structured support vector machine (SSVM) is proposed. Through the SSVM, the overlap rate between the candidate moving target features and the actual moving target is output. The estimated position of the current video sequence frame is obtained according to the maximum overlap. Sample the frame target in the video sequence, and realize the occlusion target tracking in tennis motion video by cycling the above process. The experimental results show that the accuracy of feature extraction of this method is about 90%. After adding the weight distribution mechanism, the convergence speed is fast, and the mean square error is about 0.2, indicating that the method has a high accuracy of feature extraction. In the detection of overlap rate, the highest overlap rate is 0.912, indicating that the method has a high accuracy of overlap rate. In addition, in the tennis motion video occlusion target tracking and detection, it can accurately locate the target position, and surround all parts of the target. There will be no redundant blank in the target box, which fully reflects the accuracy and robustness of tennis motion video occlusion target tracking. [ABSTRACT FROM AUTHOR]
ISSN:01291564
DOI:10.1142/S0129156425402049