TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917646831714304 |
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| author | Scott, Atom Uchida, Ikuma Ding, Ning Umemoto, Rikuhei Bunker, Rory Kobayashi, Ren Koyama, Takeshi Onishi, Masaki Kameda, Yoshinari Fujii, Keisuke |
| author_facet | Scott, Atom Uchida, Ikuma Ding, Ning Umemoto, Rikuhei Bunker, Rory Kobayashi, Ren Koyama, Takeshi Onishi, Masaki Kameda, Yoshinari Fujii, Keisuke |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos Scott, Atom Uchida, Ikuma Ding, Ning Umemoto, Rikuhei Bunker, Rory Kobayashi, Ren Koyama, Takeshi Onishi, Masaki Kameda, Yoshinari Fujii, Keisuke Computer Vision and Pattern Recognition 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/. |
| title | TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.13868 |