Football referee gesture recognition algorithm based on YOLOv8s

• Published in: Frontiers in computational neuroscience Vol. 18; p. 1341234
• Main Authors: Yang, Zhiyuan, Shen, Yuanyuan, Shen, Yanfei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 19.02.2024; Frontiers Media S.A
• Subjects: Accuracy; Algorithms; Basketball; Cameras; Data processing; Decision making; Deep learning; Electromyography; Football; football gesture recognition; GAM; MDPIoU; Neuroscience; P2 detection head; Sensors; Sports officiating; Wearable computers; YOLOv8s; deep learning; GAM; football gesture recognition; YOLOv8s; MDPIoU; P2 detection head
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2024.1341234

Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.