Basketball Action Recognition Based on the Combination of YOLO and a Deep Fuzzy LSTM Network

Abstract

The ability to identify human actions in uncontrolled surroundings is important for human-computer interaction (HCI), especially in the sports areas to offer athletes, coaches, and analysts valuable knowledge about movement techniques and aid referees in making well-informed decisions regarding sports movements. Noteworthy, recognizing human actions in the context of basketball sports remains a difficult task due to issues like intricate backgrounds, obstructed actions, and inconsistent lighting conditions. Accordingly, a method based on the combination of YOLO and deep fuzzy LSTM network is proposed in this paper. YOLO is utilized for detecting players in the frame and the combination of LSTM and fuzzy logic is used to perform the final classification. The reason behind using LSTM along with fuzzy logic refers to its inability in coping with uncertainty which led to the creation of a more transparent, interpretable, and accurate predictive system. The proposed model was validated on SpaceJam and Basketball-51 datasets. Based on the empirical results, the proposed model outperformed all baseline models on both datasets which obviously confirms the priority of our combinational model for basketball action recognition.

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“…In terms of computation, our method also requires less computation than other methods. On the NBA dataset, our method requires 121.73G FLOPs, while Khobdeh et al (2023)'s method requires 391.25G. This means that our method can perform faster training and inference on the same hardware conditions.…”