---
tags:
- pixel-tracking
- computer-vision
license: mit
library: pytorch
inference: false
---

# PIPs: Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories

* Model Authors: Adam W Harley and Zhaoyuan Fang and Katerina Fragkiadaki
* Paper: Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories (ECCV 2022 - https://arxiv.org/abs/2204.04153
* Code Repo: https://github.com/aharley/pips
* Project Homepage: https://particle-video-revisited.github.io

From the paper abstract:

> [...] we revisit Sand and Teller's "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions.

![](https://particle-video-revisited.github.io/images/fig1.jpg)

# Citation

```
@inproceedings{harley2022particle,
  title={Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories},
  author={Adam W Harley and Zhaoyuan Fang and Katerina Fragkiadaki},
  booktitle={ECCV},
  year={2022}
}
```