TrackTacular 🐙

Lifting Multi-View Detection and Tracking to the Bird’s Eye View

Torben Teepe, Philipp Wolters, Johannes Gilg, Fabian Herzog, Gerhard Rigoll

Tip

This work is an extension of your previous work EarlyBird 🦅. Feel free to check it out and extend our multi-view object detection and tracking pipeline on other datasets!

Usage

Getting Started

1. Install PyTorch with CUDA support

   pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu118

2. Install mmcv with CUDA support

   pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.1/index.html

3. Install remaining dependencies

   pip install -r requirements.txt

Training

python world_track.py fit -c configs/t_fit.yml \
    -c configs/d_{multiviewx,wildtrack,synthehicle}.yml \
    -c configs/m_{mvdet,segnet,liftnet,bevformer}.yml

Testing

python world_track.py test -c model_weights/config.yaml \
    --ckpt model_weights/model-epoch=35-val_loss=6.50.ckpt

Acknowledgement

• Simple-BEV: Adam W. Harley
• MVDeTr: Yunzhong Hou

Citation

@article{teepe2023lifting,
      title={Lifting Multi-View Detection and Tracking to the Bird's Eye View}, 
      author={Torben Teepe and Philipp Wolters and Johannes Gilg and Fabian Herzog and Gerhard Rigoll},
      year={2024},
      eprint={2403.12573},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}