Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach

Introduction

This is the next version of our DyGLIP to solve the multi-camera multi-object tracking problem on autonomous vehicles.

Implementation

The implementation will be available soon.

Citations

If you find this code useful for your research, please consider citing:

@InProceedings{Quach_2021_CVPR,
    author    = {Quach, Kha Gia and Nguyen, Pha and Le, Huu and Truong, Thanh-Dat and Duong, Chi Nhan and Tran, Minh-Triet and Luu, Khoa},
    title     = {DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13784-13793}
}

Paper Linkage

@InProceedings{Nguyen_2022_CVPR,
    author    = {Nguyen, Pha and Quach, Kha Gia and Duong, Chi Nhan and Le, Ngan and Nguyen, Xuan-Bac and Luu, Khoa},
    title     = {Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2022},
    pages     = {2569-2578}
}

Paper Linkage

@article{Nguyen_2022_multi,
    title        = {{Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach}},
    author       = {Nguyen*, Pha and Quach*, Kha Gia and Duong, Chi Nhan and Phung, Son Lam and Le, Ngan and Luu, Khoa},
    year         = {2022}
}