{"id":20208061,"url":"https://github.com/detectrecog/ccp","last_synced_at":"2025-06-29T04:41:56.580Z","repository":{"id":111966007,"uuid":"390270071","full_name":"detectRecog/CCP","owner":"detectRecog","description":"PointTrackV2(TPAMI2021) \u0026\u0026 CCP(ICCV2021)","archived":false,"fork":false,"pushed_at":"2021-09-29T08:08:51.000Z","size":7323,"stargazers_count":20,"open_issues_count":3,"forks_count":4,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-10T13:10:04.942Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/detectRecog.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-07-28T08:15:57.000Z","updated_at":"2024-08-02T17:58:23.000Z","dependencies_parsed_at":"2023-07-09T01:15:48.040Z","dependency_job_id":null,"html_url":"https://github.com/detectRecog/CCP","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/detectRecog/CCP","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/detectRecog%2FCCP","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/detectRecog%2FCCP/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/detectRecog%2FCCP/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/detectRecog%2FCCP/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/detectRecog","download_url":"https://codeload.github.com/detectRecog/CCP/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/detectRecog%2FCCP/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262538611,"owners_count":23325813,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-14T05:33:57.120Z","updated_at":"2025-06-29T04:41:56.546Z","avatar_url":"https://github.com/detectRecog.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## PointTrackV2 \u0026\u0026 CCP\n\n\nThis codebase implements **PointTrackV2 (TPAMI 2021)** and **CCP(ICCV 2021)**, a highly effective framework for multi-object tracking and segmentation (MOTS) described in: \n\n```\n@ARTICLE{9449985,\n  author={Xu, Zhenbo and Yang, Wei and Zhang, Wei and Tan, Xiao and Huang, Huan and Huang, Liusheng},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, \n  title={Segment as Points for Efficient and Effective Online Multi-Object Tracking and Segmentation}, \n  year={2021},\n  volume={},\n  number={},\n  pages={1-1},\n  doi={10.1109/TPAMI.2021.3087898}}\n@inproceedings{xu2021continuous,\n  title={Continuous Copy-Paste for One-stage Multi-object Tracking and Segmentation},\n  author={Xu, Zhenbo and Meng, Ajin and Yang, Wei and Huang, Liusheng},\n  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n  pages={6222--6231},\n  year={2019}\n}\n```\n\n**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.**\n\nOur network architecture adopts [SpatialEmbedding](https://github.com/davyneven/SpatialEmbeddings) as the segmentation sub-network. \nThe 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.\nThe detailed task description of MOTS is avaliable in [MOTS challenge](https://www.vision.rwth-aachen.de/page/mots).  \n\n\n## Getting started\n\nThis codebase showcases the proposed framework named PointTrack for MOTS using the KITTI MOTS dataset. \n\n### Prerequisites\nDependencies, please refer to 'pt17.yml' \n\nNote 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. \nThe structure under **kittiRoot** should looks like:\n\n```\nkittiRoot\n│   images -\u003e training/image_02/ \n│   instances\n│   │    0000\n│   │    0001\n│   │    ...\n│   training\n│   │   image_02\n│   │   │    0000\n│   │   │    0001\n│   │   │    ...  \n│   testing\n│   │   image_02\n│   │   │    0000\n│   │   │    0001\n│   │   │    ... \n```\n\n## Contact\nIf you find problems in the code, please open an issue.\n\nFor general questions, please contact the corresponding author Wei Yang (qubit@ustc.edu.cn).\n\n\n## License\n\nThis 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/).\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdetectrecog%2Fccp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdetectrecog%2Fccp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdetectrecog%2Fccp/lists"}