Player Tracking

Player tracking in sports involves automatically identifying and tracking individual players within video footage of games, aiming to provide quantitative data on player movement and interactions. Current research focuses on improving the accuracy and robustness of tracking algorithms, particularly addressing challenges like occlusions, rapid movements, and similar appearances, often employing deep learning architectures such as object detectors (e.g., YOLO) and trackers combined with graph neural networks or homographic projections to handle complex scenarios. This technology offers significant advancements in sports analytics, enabling more detailed performance analysis for coaches and athletes, enhanced viewer experiences through virtual or augmented reality, and improved accessibility of advanced analytics to teams with limited resources.