IPapakis / GCNNMatch

This repository is the official code implementation of the paper GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization.

GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

This repository is the official code implementation of the GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization on IEEE and on Arxiv. Link to access a new traffic vehicle monitoring dataset named "VA Beach Traffic Dataset" will be provided here.

Citing:

If you find this paper or code useful, please cite using the following on IEEE:

@inproceedings{papakis2021graph,
  title={A Graph Convolutional Neural Network Based Approach for Traffic Monitoring Using Augmented Detections with Optical Flow},
  author={Papakis, Ioannis and Sarkar, Abhijit and Karpatne, Anuj},
  booktitle={2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},
  pages={2980--2986},
  year={2021},
  organization={IEEE}
}

or on Arxiv:

@article{papakis2020gcnnmatch,
  title={GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization},
  author={Papakis, Ioannis and Sarkar, Abhijit and Karpatne, Anuj},
  journal={arXiv preprint arXiv:2010.00067},
  year={2020}
}

Installing & Preparation:

• Install singularity following instructions from its website.

• Git clone this repo folder and cd to it.

• "sudo singularity build geometric.sif singularity". Follow instructions from pytorch-geometric to change settings if needed for your system.

• Download MOT17 Dataset from MOT website and place it in a folder /MOT_dataset.

• "mkdir overlay". It will allow you to install additional packages if needed in the future.

• "sudo singularity run --nv -B /MOT_dataset/:/data --overlay overlay/ geometric.sif"

• "./create_folders.sh"

Training:

• Command: ./train.sh

• Result: Training will start and save the trained models in /models. Settings can be changed in tracking.py.

Testing:

• Specify which trained model to use in tracking.py. A trained model can be found here.

• Command: ./test.sh

• Result: Testing will start and produce txt files and videos saved in /output. Settings can be changed in tracking.py

For Benchmark evaluation the pre-processed with Tracktor detection files from this repo were used.