SportSBD: Shot Boundary Detection in Sports Footage

Shot Boundary Detection (SBD), which identifies scene (or “shot”) changes, is a core step in video analysis pipelines such as summarization and highlight generation. Yet, it remains challenging in sports broadcasts because rapid camera motion, frequent camera switches, sport-specific transitions and graphic overlays often cause false detections and poor cross-domain generalization. In this paper, we address shot boundary detection in professional sports broadcasts, focusing on ice hockey and soccer. We propose a sports-oriented model based on a fine-tuned R(2+1)D 3D CNN, trained to detect hard cuts, gradual transitions, and logo-based replay effects. The model is evaluated on both goal-centered clips and full-match broadcast footage, and is benchmarked against two state-of-the-art baselines: TransNetV2 for accuracy and PySceneDetect for efficiency. Our approach consistently achieves higher precision, recall, and F1-score across all evaluation settings, while demonstrating strong cross-league generalization within ice hockey and cross-sport generalization to soccer. We release our pretrained sports-specific SBD model as an open-source Python package, enabling straightforward integration into existing video analysis pipelines.