Athlete Detection Algorithm Based on Multi-scale Linear Global Attention
The rapid movement and frequent occlusion of athletes during a competition make it difficult to detect athletes in a video,along with causing multiple detections,a decline in the detection accuracy,and other problems.The current mainstream detection methods do not perform well for athlete detection under moving and occluding conditions.When the athletes are occluded,the size of the bounding box increases.In this study,a cutout is introduced as a data augmentation method to simulate occlusion,and an athlete detection algorithm based on a multi-scale linear global attention EfficientViT module is constructed.Specifically,the linear global attention module is used to reduce the amount of computation,and the convolution module is supplemented to enhance its local feature extraction capability.The tokens for different attention heads are aggregated through lightweight small convolution to obtain multi-scale information and enhance its global feature extraction capability.EIoU is selected as the bounding box loss for the loss function,with the width and height distances between the detection bounding box and target bounding box added.Thus,the detection and real target bounding boxes are closer in scale.The results of an experiment on four publicly available basketball game video datasets from the SportsMOT dataset show that the proposed algorithm can achieve a precision of 98.0%and mean Average Precision(mAP)of 98.2%.The precision and high-confidence mAP of the proposed algorithm are 4%and 8.7%higher,respectively,than that of the original YOLOv5 algorithm.
YOLOv5 algorithmathlete detectionmulti-scale linear global attentiondata augmentationbounding box loss
国家科技期刊平台
NSTL
万方数据
