基于损失加权的实时篮球裁判手势识别系统

doi:10.12066/j.issn.1007-2861.2422

Abstract

Abstract:

To help the audience better understand the meaning of the referee's gesture when watching a live broadcast or a video of a basketball game, or to help video analysts analyze the game video, a real-time basketball referee gesture detection and recognition system, Yolov5-Basketball Referee (Yolov5-BR), was designed. The Yolov5 target detection algorithm was used as the basic model, and the intersection over union (IoU) and complete IoU (CIoU) loss functions of its boundary box were weighted to enhance the robustness of the prediction box. After the C3 module, an attention mechanism was added to generate more distinguishing feature representation and improve the network recognition performance. In addition, an adaptive feature fusion mechanism was incorporated into the head of the detection layer to make full use of the high-level semantic information of the image. Finally, the target confidence loss function was weighted unequally to improve the robustness of small-target detection. On a self-made referee gesture dataset, Yolov5-BR achieved a 95.4% mAP value, with a local video detection rate of 55.5 frame/s, an external camera resolution of $1 280 \times 960$, and a detection rate of 25 frame/s. Experimental results show that, compared with the original model, Yolov5-BR can effectively improve the performance of judging gestures while maintaining high accuracy, stability, and real-time response.

Key words: target detection, gesture recognition, basketball referee, deep learning, loss function

CLC Number:

TP391

LI Zhongyu, SUN Haodong, LI Jiao. Design of real-time basketball referee gesture recognition system based on loss weighting[J]. Journal of Shanghai University（Natural Science Edition）, 2023, 29(1): 68-81.

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URL: https://www.journal.shu.edu.cn/EN/10.12066/j.issn.1007-2861.2422

https://www.journal.shu.edu.cn/EN/Y2023/V29/I1/68

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Design of real-time basketball referee gesture recognition system based on loss weighting

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