Is This Tracker on? a Benchmark Protocol for Dynamic Tracking

Abstract

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.

Cite

Text

Demler et al. "Is This Tracker on? a Benchmark Protocol for Dynamic Tracking." Advances in Neural Information Processing Systems, 2025.

Markdown

[Demler et al. "Is This Tracker on? a Benchmark Protocol for Dynamic Tracking." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/demler2025neurips-tracker/)

BibTeX

@inproceedings{demler2025neurips-tracker,
  title     = {{Is This Tracker on? a Benchmark Protocol for Dynamic Tracking}},
  author    = {Demler, Ilona and Chauhan, Saumya and Gkioxari, Georgia},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2025},
  url       = {https://mlanthology.org/neurips/2025/demler2025neurips-tracker/}
}