  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/.
