# Robust Instance Tracking via Uncertainty Flow

arXiv: https://arxiv.org/abs/2010.04367 · HTML: https://mlsanity.com/paper/2010.04367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Accurate Dense Correspondences and When to Trust Them](https://mlsanity.com/paper/2101.01710/) (2021)
- [PDC-Net+: Enhanced Probabilistic Dense Correspondence Network](https://mlsanity.com/paper/2109.13912/) (2021)

## Abstract (arXiv)

Current state-of-the-art trackers often fail due to distractorsand large object appearance changes. In this work, we explore the use ofdense optical flow to improve tracking robustness. Our main insight is that, because flow estimation can also have errors, we need to incorporate an estimate of flow uncertainty for robust tracking. We present a novel tracking framework which combines appearance and flow uncertainty information to track objects in challenging scenarios. We experimentally verify that our framework improves tracking robustness, leading to new state-of-the-art results. Further, our experimental ablations shows the importance of flow uncertainty for robust tracking.