Ryosuke Hori

As data analysis becomes increasingly significant, streamlining data collection is essential, particularly in sports. Among various aspects, player performance analysis is crucial for both professional and amateur levels. In computer vision, Multi-Object Tracking (MOT) and Multi-Camera Multi-Object Tracking (MCMOT) enable the analysis of people’s trajectories captured in videos. However, many existing methods do not account for crowded scenarios, such as sports matches, and require complex preprocessing of datasets, making them impractical for real-world applications. To address these challenges, we introduce a simple yet novel extension of MOT methods by utilizing the Gaussian distribution to associate tracking information obtained from multiple cameras. By integrating tracking data from all cameras, our methodology effectively mitigates identity switches and expands the tracking range. Experimental results demonstrate our tracking system’s effectiveness, showing a reduction in ID switches and false negatives compared to results achieved with a standard multi-object tracker.

Video analysis has become an essential aspect of net sports, such as badminton. Accurately predicting the future trajectory of balls and shuttlecocks can significantly benefit players by enhancing their performance and enabling them to devise effective game strategies. This paper aims to analyze data to provide players with an advantage in the fast-paced rallies of badminton matches. The paper delves into the innovative task of predicting future shuttlecock trajectories in badminton match videos and presents a method that takes into account both the shuttlecock position and the positions and postures of the players. In the experiments, players were extracted from the match video, their postures were analyzed, and a time-series model was trained. The results indicate that the proposed method improved accuracy by 13% compared to methods that solely used shuttlecock position information as input, and by 8.4% compared to methods that employed both shuttlecock and player position information as input.