readme: ---
  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}
  }
  ```
  