eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915720526299136 |
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| author | Vinod, Krishna Vishal, Joseph Raj Chanda, Kaustav Ramesh, Prithvi Jai Yang, Yezhou Chakravarthi, Bharatesh |
| author_facet | Vinod, Krishna Vishal, Joseph Raj Chanda, Kaustav Ramesh, Prithvi Jai Yang, Yezhou Chakravarthi, Bharatesh |
| contents | Tracking skiers in RGB broadcast footage is challenging due to motion blur, static overlays, and clutter that obscure the fast-moving athlete. Event cameras, with their asynchronous contrast sensing, offer natural robustness to such artifacts, yet a controlled benchmark for winter-sport tracking has been missing. We introduce event SkiTB (eSkiTB), a synthetic event-based ski tracking dataset generated from SkiTB using direct video-to-event conversion without neural interpolation, enabling an iso-informational comparison between RGB and event modalities. Benchmarking SDTrack (spiking transformer) against STARK (RGB transformer), we find that event-based tracking is substantially resilient to broadcast clutter in scenes dominated by static overlays, achieving 0.685 IoU, outperforming RGB by +20.0 points. Across the dataset, SDTrack attains a mean IoU of 0.711, demonstrating that temporal contrast is a reliable cue for tracking ballistic motion in visually congested environments. eSkiTB establishes the first controlled setting for event-based tracking in winter sports and highlights the promise of event cameras for ski tracking. The dataset and code will be released at https://github.com/eventbasedvision/eSkiTB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06647 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers Vinod, Krishna Vishal, Joseph Raj Chanda, Kaustav Ramesh, Prithvi Jai Yang, Yezhou Chakravarthi, Bharatesh Computer Vision and Pattern Recognition Tracking skiers in RGB broadcast footage is challenging due to motion blur, static overlays, and clutter that obscure the fast-moving athlete. Event cameras, with their asynchronous contrast sensing, offer natural robustness to such artifacts, yet a controlled benchmark for winter-sport tracking has been missing. We introduce event SkiTB (eSkiTB), a synthetic event-based ski tracking dataset generated from SkiTB using direct video-to-event conversion without neural interpolation, enabling an iso-informational comparison between RGB and event modalities. Benchmarking SDTrack (spiking transformer) against STARK (RGB transformer), we find that event-based tracking is substantially resilient to broadcast clutter in scenes dominated by static overlays, achieving 0.685 IoU, outperforming RGB by +20.0 points. Across the dataset, SDTrack attains a mean IoU of 0.711, demonstrating that temporal contrast is a reliable cue for tracking ballistic motion in visually congested environments. eSkiTB establishes the first controlled setting for event-based tracking in winter sports and highlights the promise of event cameras for ski tracking. The dataset and code will be released at https://github.com/eventbasedvision/eSkiTB. |
| title | eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.06647 |