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

TL;DR

TeamTrack introduces a comprehensive dataset for multi-object tracking in full-pitch sports videos, aiming to improve tracking accuracy in complex, dynamic team sports scenarios.

Contribution

The paper presents a new, extensive dataset for multi-object tracking in sports, along with benchmarking results demonstrating its utility and potential to advance the field.

Findings

Dataset covers multiple sports including soccer, basketball, and handball.

Benchmark results highlight challenges and opportunities in sports MOT.

Resource availability promotes further research in complex tracking scenarios.

Abstract

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/.