Real-Time Ball Tracking and Action Classification using an Event Camera

In many sports events, replay footage is commonly used to enhance audience engagement. However, the process of selecting and editing these replays is typically manual, placing a significant operational burden on on-site staff. While recent advancements in computer vision have facilitated sports match analysis, most existing methods lack real-time capabilities, rendering them unsuitable for automatic replay generation. This paper proposes a real-time ball tracking and action classification method using an event camera for volleyball matches, addressing the gap in automatic replay generation. By leveraging the unique advantages of event cameras, our method avoids machine learning techniques, instead utilizing event-based data streams for lightweight computation in ball tracking and action classification. When ball identification fails, temporal and spatial patterns of key events in volleyball are used for localization. The tracked ball trajectory and classified actions are then employed to identify key moments for replay generation. Experimental results demonstrate the effectiveness of the proposed method, achieving real-time operation and successfully identifying moments suitable for replay generation.