Fusion Enhancement of YOLOv5 and Copula Bayesian Classifier for Hand Gesture Recognition in Smart Sports Venues

This study aims addresses the challenge of gesture recognition in smart sports stadiums through the fusion enhancement of YOLOv5 and Copula Bayesian Classifier algorithm. By leveraging FasterNet for YOLOv5 transformation, integrating the Convolutional Block Attention Module (CBAM) as an attention mechanism, and merging it with the Copula Bayesian Classifier, we propose a tandem model for robust gesture recognition in the complex environments of sports venues. Extensive training on a substantial dataset yields an efficient and accurate gesture recognition model. Our findings showcase the effectiveness of the proposed algorithm, achieving a remarkable accuracy rate of 99.2% in identifying gesture categories swiftly. This advancement holds significant implications for future human-computer interaction in smart sports stadiums, offering a more seamless and intelligent mode of interaction for both spectators and athletes. Ultimately, it enhances user experience and management efficiency within smart sports venues.