PointTrackV2 && CCP

This codebase implements PointTrackV2 (TPAMI 2021) and CCP(ICCV 2021), a highly effective framework for multi-object tracking and segmentation (MOTS) described in:

@ARTICLE{9449985,
  author={Xu, Zhenbo and Yang, Wei and Zhang, Wei and Tan, Xiao and Huang, Huan and Huang, Liusheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Segment as Points for Efficient and Effective Online Multi-Object Tracking and Segmentation}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3087898}}
@inproceedings{xu2021continuous,
  title={Continuous Copy-Paste for One-stage Multi-object Tracking and Segmentation},
  author={Xu, Zhenbo and Meng, Ajin and Yang, Wei and Huang, Liusheng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6222--6231},
  year={2019}
}

PointTrackV2 presents a new learning strategy for pixel-wise feature learning on the 2D image plane, which has proven to be effective for instance association.

Our network architecture adopts SpatialEmbedding as the segmentation sub-network. The current ranking of PointTrack is available in KITTI leader-board. Until now (07/03/2020), PointTrack++ still ranks first for both cars and pedestrians. The detailed task description of MOTS is avaliable in MOTS challenge.

Getting started

This codebase showcases the proposed framework named PointTrack for MOTS using the KITTI MOTS dataset.

Prerequisites

Dependencies, please refer to 'pt17.yml'

Note that the scripts for evaluation is included in this repo. After images and instances (annotations) are downloaded, put them under kittiRoot and change the path in repoRoot/config.py accordingly. The structure under kittiRoot should looks like:

kittiRoot
│   images -> training/image_02/ 
│   instances
│   │    0000
│   │    0001
│   │    ...
│   training
│   │   image_02
│   │   │    0000
│   │   │    0001
│   │   │    ...  
│   testing
│   │   image_02
│   │   │    0000
│   │   │    0001
│   │   │    ... 

Contact

If you find problems in the code, please open an issue.

For general questions, please contact the corresponding author Wei Yang (qubit@ustc.edu.cn).

License

This software is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here.