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This is the official implementation of TransCenter. The code and pretrained models are now available here: https://gitlab.inria.fr/yixu/TransCenter_official.

Home Page: https://team.inria.fr/robotlearn/transcenter-transformers-with-dense-queriesfor-multiple-object-tracking/

License: Other

transcenter's Introduction

TransCenter: Transformers with Dense Queries for Multiple-Object Tracking

TransCenter's code is now available, you can find the code and pretrained models at https://gitlab.inria.fr/yixu/TransCenter_official.

TransCenter: Transformers with Dense Queries for Multiple-Object Tracking
Yihong Xu, Yutong Ban, Guillaume Delorme, Chuang Gan, Daniela Rus, Xavier Alameda-Pineda
[Paper] [Project]

Bibtex

If you find this code useful, please star the project and consider citing:

@misc{xu2021transcenter,
      title={TransCenter: Transformers with Dense Queries for Multiple-Object Tracking}, 
      author={Yihong Xu and Yutong Ban and Guillaume Delorme and Chuang Gan and Daniela Rus and Xavier Alameda-Pineda},
      year={2021},
      eprint={2103.15145},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

The code for TransCenter is modified and network pre-trained weights are obtained from the following repositories:

  1. The Person Re-ID Network (./tracking/transcenter/model_zoo/ResNet_iter_25245.pth) is from Tracktor.
  2. The lightflownet pretrained model (./tracking/transcenter/util/LiteFlownet/network-kitti.pytorch) is from pytorch-liteflownet and LiteFlowNet.
  3. The deformable transformer pretrained model (./model_zoo/r50_deformable_detr-checkpoint.pth) is from Deformable-DETR.
  4. The data format conversion code is modified from CenterTrack.

CenterTrack, Deformable-DETR, Tracktor.

@article{zhou2020tracking,
  title={Tracking Objects as Points},
  author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
  journal={ECCV},
  year={2020}
}

@InProceedings{tracktor_2019_ICCV,
author = {Bergmann, Philipp and Meinhardt, Tim and Leal{-}Taix{\'{e}}, Laura},
title = {Tracking Without Bells and Whistles},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}}

@article{zhu2020deformable,
  title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
  author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
  journal={arXiv preprint arXiv:2010.04159},
  year={2020}
}

Several modules are from:

MOT Metrics in Python: py-motmetrics

Soft-NMS: Soft-NMS

DETR: DETR

DCNv2: DCNv2

correlation_package: correlation_package

pytorch-liteflownet: pytorch-liteflownet

LiteFlowNet: LiteFlowNet

@InProceedings{hui18liteflownet,
    author = {Tak-Wai Hui and Xiaoou Tang and Chen Change Loy},
    title = {LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation},
    booktitle  = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2018},
    pages = {8981--8989},
    }

transcenter's People

Contributors

ragondyn avatar yihongxu avatar

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