MvMHAT: Multi-view Multi-Human Association and Tracking

Self-supervised Multi-view Multi-Human Association and Tracking,
Yiyang Gan, Ruize Han, Liqiang Yin, Wei Feng, Song Wang

• A self-supervised learning framework for MvMHAT.
• A new benchmark for training and testing MvMHAT.

Contact: realgump@tju.edu.cn. Any questions or discussions are welcomed!

Abstract

Multi-human association and tracking (MHAT) with multi-view cameras, aims to track a group of people over time in each view, meanwhile, identify the same person across different views at the same time. This is a relatively new problem but has significance for multi-person scene video surveillance. Different from previous multiple object tracking (MOT) and multi-target multi-camera tracking (MTMCT) tasks, which only consider the over-time human association, multi-view MHAT requires to jointly achieve the cross-spatial-temporal data association. In this paper, we model this problem with a self-supervised learning framework and propose an end-to-end network to solve it. Specifically, we proposed a spatial-temporal association network with three designed self-supervised learning losses including self-similarity loss, transitive-similarity loss, and symmetrical-consistency loss, to simultaneously associate the human over time and across views. Besides, to promote the research on multi-view MHAT, we build a new large-scale benchmark for algorithm training and testing. Extensive experiments on the proposed datasets verify the effectiveness of our method.

Install

The code was tested on Ubuntu 16.04, with Anaconda Python 3.6 and PyTorch v1.7.1. NVIDIA GPUs are needed for both training and testing. After install Anaconda:

0. [Optional but recommended] create a new conda environment：

   conda create -n MVMHAT python=3.6

And activate the environment:

   conda activate MVMHAT

1. Install pytorch:

   conda install pytorch=1.7.1 torchvision -c pytorch

2. Clone the repository:

   MVMHAT_ROOT=/path/to/clone/MVMHAT
   git clone https://github.com/realgump/MvMHAT.git $MVMHAT_ROOT

3. Install the requirements:

   pip install -r requirements.txt

4. Download the pretrained model to promote convergence:

   cd $MVMHAT_ROOT/models
   wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O pretrained.pth

Dataset

1. Self-collected video & label:

• Link: Baidu Netdisk

• Password：2cfh

2. Campus video & label:

• Link: Baidu Netdisk

• Password: 8sg9

3. EPFL video & label:

• Link: Baidu Netdisk

• Password: jjaw

4. [NEW] The complete dataset is also available via:

• Link: OneDrive

• Password: MvMHAT

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{gan2021mvmhat,
  title={Self-supervised Multi-view Multi-Human Association and Tracking},
  author={Yiyang Gan, Ruize Han, Liqiang Yin, Wei Feng, Song Wang},
  booktitle={ACM MM},
  year={2021}
}

References

• Portions of the code are borrowed from Deep SORT, thanks for their great work.
• Portions of the videos are borrowed from Campus and EPFL, thanks for their contributions.

More information is coming soon ...