新基准ITTO揭示点跟踪在真实动态场景中的致命缺陷。
Is This Tracker On? A Benchmark Protocol for Dynamic Tracking
- 构建包含复杂运动与遮挡的多阶段标注视频集
- 现有追踪器在遮挡后重识别准确率显著下降
- 适合评估真实场景下追踪算法鲁棒性
我们提出ITTO，一个面向点跟踪方法的挑战性新基准套件。视频数据源自现有数据集和第一人称真实录制，通过多阶段流程获取高质量人工标注。ITTO捕捉了真实场景中的运动复杂性、遮挡模式与对象多样性——这些特征在现有基准中普遍缺失。我们在ITTO上对最先进追踪方法进行严格分析，按运动复杂性维度拆解性能表现。结果表明，现有追踪器在此类挑战下表现不佳，尤其在遮挡后重识别方面存在明显失效模式，凸显了对适配真实动态的新建模方法的迫切需求。我们期望ITTO成为推进点追踪研究的基础测试平台，指导更鲁棒追踪算法的开发。
原文摘要 · Abstract (English)
We introduce ITTO, a challenging new benchmark suite for evaluating and diagnosing the capabilities and limitations of point tracking methods. Our videos are sourced from existing datasets and egocentric real-world recordings, with high-quality human annotations collected through a multi-stage pipeline. ITTO captures the motion complexity, occlusion patterns, and object diversity characteristic of real-world scenes -- factors that are largely absent in current benchmarks. We conduct a rigorous analysis of state-of-the-art tracking methods on ITTO, breaking down performance along key axes of motion complexity. Our findings reveal that existing trackers struggle with these challenges, particularly in re-identifying points after occlusion, highlighting critical failure modes. These results point to the need for new modeling approaches tailored to real-world dynamics. We envision ITTO as a foundation testbed for advancing point tracking and guiding the development of more robust tracking algorithms.
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