针对长视频点追踪性能下降问题，提出细粒度点辨识方法提升追踪精度。
Solution for Point Tracking Task of ECCV 2nd Perception Test Challenge 2024
- 通过多粒度感知识别静态视频中的点特征
- 动态轨迹修正使静态点追踪准确率达0.4720
- 适合静态相机拍摄的长视频点追踪任务
本报告提出一种改进的点追踪方法（Fine-grained Point Discrimination, FPD），用于在视频中追踪物理表面的点。尽管现有方法在短序列中表现良好，但在长序列场景下仍存在性能下降和资源开销大的问题。FPD通过两个关键组件解决此问题：(1) 多粒度点感知，可检测视频中的静态序列及对应点；(2) 动态轨迹修正，根据追踪点类型替换其轨迹。该方法在2024年ECCV第二届感知挑战赛最终测试中获得0.4720的得分，位列第二。
原文摘要 · Abstract (English)
This report introduces an improved method for the Tracking Any Point~(TAP), focusing on monitoring physical surfaces in video footage. Despite their success with short-sequence scenarios, TAP methods still face performance degradation and resource overhead in long-sequence situations. To address these issues, we propose a simple yet effective approach called Fine-grained Point Discrimination~(\textbf{FPD}), which focuses on perceiving and rectifying point tracking at multiple granularities in zero-shot manner, especially for static points in the videos shot by a static camera. The proposed FPD contains two key components: $(1)$ Multi-granularity point perception, which can detect static sequences in video and points. $(2)$ Dynamic trajectory correction, which replaces point trajectories based on the type of tracked point. Our approach achieved the second highest score in the final test with a score of $0.4720$.
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