https://github.com/detectrecog/ccp
PointTrackV2(TPAMI2021) && CCP(ICCV2021)
https://github.com/detectrecog/ccp
Last synced: 12 months ago
JSON representation
PointTrackV2(TPAMI2021) && CCP(ICCV2021)
- Host: GitHub
- URL: https://github.com/detectrecog/ccp
- Owner: detectRecog
- Created: 2021-07-28T08:15:57.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-29T08:08:51.000Z (over 4 years ago)
- Last Synced: 2025-04-10T13:10:04.942Z (about 1 year ago)
- Language: Python
- Size: 6.98 MB
- Stars: 20
- Watchers: 1
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 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](https://github.com/davyneven/SpatialEmbeddings) as the segmentation sub-network.
The current ranking of PointTrack is available in [KITTI leader-board](http://www.cvlibs.net/datasets/kitti/eval_mots.php). 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](https://www.vision.rwth-aachen.de/page/mots).
## 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](http://creativecommons.org/licenses/by-nc/4.0/).