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

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…
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Taxonomy
TopicsVideo Analysis and Summarization · Video Surveillance and Tracking Methods
