TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos

Atom Scott, Ikuma Uchida, Ning Ding, Rikuhei Umemoto, Rory Bunker, Ren Kobayashi, Takeshi Koyama, Masaki Onishi, Yoshinari Kameda, Keisuke Fujii; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3357-3366

Multi-object tracking (MOT) is a critical and challenging task in computer vision particularly in situations involving objects with similar appearances but diverse movements as seen in team sports. Current methods largely reliant on object detection and appearance often fail to track targets in such complex scenarios accurately. This limitation is further exacerbated by the lack of comprehensive and diverse datasets covering the full view of sports pitches. Addressing these issues we introduce TeamTrack a pioneering benchmark dataset specifically designed for MOT in sports. TeamTrack is an extensive collection of full-pitch video data from various sports including soccer basketball and handball. Furthermore we perform a comprehensive analysis and benchmarking effort to underscore TeamTrack's utility and potential impact. Our work signifies a crucial step forward promising to elevate the precision and effectiveness of MOT in complex dynamic settings such as team sports. The dataset project code and competition is released at: https://atomscott.github.io/TeamTrack/.