DiffusionTrack：Diffusion Model For Multi-Object Tracking

DiffusionTrack is the first work of diffusion model for multi-object tracking.

DiffusionTrack：Diffusion Model For Multi-Object Tracking

Run Luo, Zikai Song, Lintao Ma, Jinlin Wei

arXiv 2308.09905

Tracking performance

Results on MOT17 challenge test set with 15.89 FPS

Method	MOTA	IDF1	HOTA	AssA	DetA
TrackFormer	74.1	68.0	57.3	54.1	60.9
MeMOT	72.5	69.0	56.9	55.2	/
MOTR	71.9	68.4	57.2	55.8	/
CenterTrack	67.8	64.7	52.2	51.0	53.8
PermaTrack	73.8	68.9	55.5	53.1	58.5
TransCenter	73.2	62.2	54.5	49.7	60.1
GTR	75.3	71.5	59.1	57.0	61.6
TubeTK	63.0	58.6	/	/	/
DiffusionTrack	77.9	73.8	60.8	58.8	63.2

Results on MOT20 challenge test set with 13.37 FPS

Method	MOTA	IDF1	HOTA	AssA	DetA
TrackFormer	68.6	65.7	54.7	53.0	56.7
MeMOT	63.7	66.1	54.1	55.0	/
TransCenter	67.7	58.7	/	/	/
DiffusionTrack	72.8	66.3	55.3	51.3	59.9

Results on Dancetrack challenge test set with 21.05 FPS

Method	MOTA	IDF1	HOTA	AssA	DetA
TransTrack	88.4	45.2	45.5	27.5	75.9
CenterTrack	86.8	35.7	41.8	22.6	78.1
DiffusionTrack	89.3	47.5	52.4	33.5	82.2

Visualization results

Robustness to detection perturbation

Installation

Step1. Install requirements for DiffusionTrack.

git clone https://github.com/RainBowLuoCS/DiffusionTrack.git
cd DiffusionTrack
pip3 install -r requirements.txt
python3 setup.py develop

Step2. Install pycocotools.

pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

Step3. Others

pip3 install cython_bbox

Step4. Install detectron2

git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

Data preparation

Download MOT17, MOT20, CrowdHuman, Cityperson, ETHZ ,Dancetrack put them under <DiffusionTrack_HOME>/datasets in the following structure:

datasets
   |——————mot
   |        └——————train
   |        └——————test
   └——————crowdhuman
   |         └——————Crowdhuman_train
   |         └——————Crowdhuman_val
   |         └——————annotation_train.odgt
   |         └——————annotation_val.odgt
   └——————MOT20
   |        └——————train
   |        └——————test
   └——————dancetrack
   |        └——————train
   |        └——————test
   └——————Cityscapes
   |        └——————images
   |        └——————labels_with_ids
   └——————ETHZ
            └——————eth01
            └——————...
            └——————eth07

Then, you need to turn the datasets to COCO format and mix different training data:

cd <DiffusionTrack_HOME>
python3 tools/convert_mot17_to_coco.py
python3 tools/convert_dancetrack_to_coco.py
python3 tools/convert_mot20_to_coco.py
python3 tools/convert_crowdhuman_to_coco.py
python3 tools/convert_cityperson_to_coco.py
python3 tools/convert_ethz_to_coco.py

Before mixing different datasets, you need to follow the operations in mix_xxx.py to create a data folder and link. Finally, you can mix the training data:

cd <DiffusionTrack_HOME>
python3 tools/mix_data_ablation.py
python3 tools/mix_data_test_mot17.py
python3 tools/mix_data_test_mot20.py

Model zoo

You can download our model weight from our model zoo. We provide a 32-bit precision model, you can load it and then use half-precision fine-tuning to get a 16-bit precision model weight, so that you will get the above inference speed.

Training

The pretrained YOLOX model can be downloaded from their model zoo. After downloading the pretrained models, you can put them under <DiffusionTrack_HOME>/pretrained.

• Train ablation model (MOT17 half train and CrowdHuman)

cd <DiffusionTrack_HOME>
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_det_mot17_ablation.py -d 8 -b 16 -o -c pretrained/bytetrack_ablation.pth.tar
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_track_mot17_ablation.py -d 8 -b 16 -o -c pretrained/diffusiontrack_ablation_det.pth.tar

• Train MOT17 test model (MOT17 train, CrowdHuman, Cityperson and ETHZ)

cd <DiffusionTrack_HOME>
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_det_mot17.py -d 8 -b 16 -o -c pretrained/bytetrack_x_mot17.pth.tar
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_track_mot17.py -d 8 -b 16 -o -c pretrained/diffusiontrack_mot17_det.pth.tar

• Train MOT20 test model (MOT20 train, CrowdHuman)

cd <DiffusionTrack_HOME>
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_det_mot20.py -d 8 -b 16 -o -c pretrained/bytetrack_x_mot20.pth.tar
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_track_mot20.py -d 8 -b 16 -o -c pretrained/diffusiontrack_mot20_det.pth.tar

Train Dancetrack test model (Dancetrack)

cd <DiffusionTrack_HOME>
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_det_dancetrack.py -d 8 -b 16 -o -c pretrained/bytetrack_x_mot17.pth.tar
python3 tools/train.py -f exps/example/mot/yolox_x_diffusion_track_dancetrack.py -d 8 -b 16 -o -c pretrained/diffusiontrack_dancetrack_det.pth.tar

Tracking

• Evaluation on MOT17 half val

cd <DiffusionTrack_HOME>
python3 tools/track.py -f exps/example/mot/yolox_x_diffusion_track_mot17_ablation.py -c pretrained/diffusiontrack_ablation_track.pth.tar -b 1 -d 1 --fuse

• Test on MOT17

cd <DiffusionTrack_HOME>
python3 tools/track.py -f exps/example/mot/yolox_x_diffusion_track_mot17.py -c pretrained/diffusiontrack_mot17_track.pth.tar -b 1 -d 1 --fuse

• Test on MOT20

cd <DiffusionTrack_HOME>
python3 tools/track.py -f exps/example/mot/yolox_x_diffusion_track_mot20.py -c pretrained/diffusiontrack_mot20_track.pth.tar -b 1 -d 1 --fuse

• Test on Dancetrack

cd <DiffusionTrack_HOME>
python3 tools/track.py -f exps/example/mot/yolox_x_diffusion_track_dancetrack.py -c pretrained/diffusiontrack_dancetrack_track.pth.tar -b 1 -d 1 --fuse

News

• (2024.02) DiffMOT is accepted by CVPR2024, demonstrating the potential of the diffusion-based tracker and once again validating our visionary insights, congratulations!
• (2023.12) Our paper is accepted by AAAI2024!
• (2023.08) Code is released!

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

Citation

If you use DiffusionTrack in your research or wish to refer to the baseline results published here, please use the following BibTeX entry.

@article{luo2023diffusiontrack,
  title={DiffusionTrack: Diffusion Model For Multi-Object Tracking},
  author={Luo, Run and Song, Zikai and Ma, Lintao and Wei, Jinlin and Yang, Wei and Yang, Min},
  journal={arXiv preprint arXiv:2308.09905},
  year={2023}
}

Acknowledgement

A large part of the code is borrowed from ByteTrack and DiffusionDet thanks for their wonderful works.