用合成事件数据集提升滑雪者追踪，显著优于传统视觉方法。
eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers
- 通过视频直接转事件数据，构建无神经插值的合成事件数据集。
- 事件追踪在静态叠加场景中达0.685 IoU，比RGB高20.0点。
- 适合关注事件相机与运动追踪融合的研究者。
在RGB广播画面中追踪滑雪者因运动模糊、静态图层和环境杂乱而困难。事件相机凭借异步对比度感知，天然具备抗干扰能力，但冬季运动追踪缺乏可控基准。我们提出事件滑雪追踪数据集eSkiTB，基于SkiTB通过直接视频转事件方式生成，不使用神经插值，实现RGB与事件模态的等信息量对比。在包含静态图层主导的场景中，基于脉冲变换器的SDTrack相比基于RGB的STARK，IoU达到0.685，领先20.0个百分点；全数据集平均IoU为0.711，表明时间对比是复杂环境中高速运动追踪的可靠线索。eSkiTB建立了冬季运动事件追踪首个可控基准，验证了事件相机在滑雪追踪中的潜力。数据集与代码将公开于https://github.com/eventbasedvision/eSkiTB。
原文摘要 · Abstract (English)
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.
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