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add ucmc track
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README.md

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- C_BIoU Track ([arxiv2211](https://arxiv.org/pdf/2211.14317v2.pdf))
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- Strong SORT ([IEEE TMM 2023](https://arxiv.org/pdf/2202.13514))
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- Sparse Track ([arxiv 2306](https://arxiv.org/pdf/2306.05238))
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- UCMC Track ([AAAI 2024](http://arxiv.org/abs/2312.08952))
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and the reid model supports:
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## 🗺️ Roadmap
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- [ x ] Add StrongSort and SparseTrack
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- [ x ] Add save video function
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- [ x ] Add timer function to calculate fps
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- [ x ] Add UCMC Track
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- [] Add more ReID modules.
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## 🔨 Installation
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- Sparse Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`
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- UCMC Track: `python tracker/track.py --dataset mot17 --detector yolox --tracker ucmctrack --kalman_format ucmc --detector_model_path weights/bytetrack_m_mot17.pth.tar --camera_parameter_folder ./tracker/cam_param_files`
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> **Important notes for UCMC Track:**
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>
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> 1. Camera parameters. The UCMC Track need the intrinsic and extrinsic parameter of camera. Please organize like the format of `tracker/cam_param_files/uavdt/M0101.txt`. One video sequence corresponds to one txt file. If you do not have the labelled parameters, you can refer to the estimating toolbox in original repo ([https://github.com/corfyi/UCMCTrack](https://github.com/corfyi/UCMCTrack)).
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>
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> 2. The code does not contain the camera motion compensation part between every two frame, please refer to [https://github.com/corfyi/UCMCTrack/issues/12](https://github.com/corfyi/UCMCTrack/issues/12). From my perspective, since the algorithm name is 'uniform', the update of compensation between every two frames is not necessary.
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### ✅ Evaluation
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Coming Soon. As an alternative, after obtaining the result txt file, you can use the [Easier to use TrackEval repo](https://github.com/JackWoo0831/Easier_To_Use_TrackEval).

README_CN.md

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- C_BIoU Track ([arxiv2211](https://arxiv.org/pdf/2211.14317v2.pdf))
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- Strong SORT ([IEEE TMM 2023](https://arxiv.org/pdf/2202.13514))
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- Sparse Track ([arxiv 2306](https://arxiv.org/pdf/2306.05238))
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- UCMC Track ([AAAI 2024](http://arxiv.org/abs/2312.08952))
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REID模型支持:
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## 🗺️ 路线图
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- [ x ] Add StrongSort and SparseTrack
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- [ x ] Add save video function
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- [ x ] Add timer function to calculate fps
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- [] Add more ReID modules.mer function to calculate fps
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- [ x ] Add UCMC Track
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- [] Add more ReID modules.
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## 🔨 安装
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- BoT-SORT: `python tracker/track.py --dataset uavdt --detector yolox --tracker botsort --kalman_format bot --detector_model_path weights/yolox_m_uavdt_50epochs.pth.tar`
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- UCMC Track: `python tracker/track.py --dataset mot17 --detector yolox --tracker ucmctrack --kalman_format ucmc --detector_model_path weights/bytetrack_m_mot17.pth.tar --camera_parameter_folder ./tracker/cam_param_files`
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>**UCMC Track的重要提示:**
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>
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> 1. 相机参数. UCMC Track需要相机的内参和外参. 请按照`tracker/cam_ram_files/uavdt/M0101.txt`的格式组织. 一个视频序列对应一个txt文件. 如果您没有标记的参数, 可以参考原始仓库中的估算工具箱([https://github.com/corfyi/UCMCTrack](https://github.com/corfyi/UCMCTrack)).
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> 2. 该代码不包含每两帧之间的相机运动补偿部分, 请参阅[https://github.com/corfyi/UCMCTrack/issues/12](https://github.com/corfyi/UCMCTrack/issues/12). 在我看来, 既然算法叫"统一相机运动补偿", 因此不需要每两帧之间再更新补偿.
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### ✅ 评估
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马上推出!作为备选项,你可以使用这个repo: [Easier to use TrackEval repo](https://github.com/JackWoo0831/Easier_To_Use_TrackEval).
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