TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
This addresses the problem of limited data for researchers and practitioners in computer vision working on multi-object tracking in complex sports scenarios, though it is incremental as it focuses on dataset creation rather than method innovation.
The authors tackled the lack of comprehensive datasets for multi-object tracking in team sports by introducing TeamTrack, a benchmark dataset covering full-pitch videos from soccer, basketball, and handball, which they analyzed and benchmarked to demonstrate its utility.
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/.