This is the repository for our paper SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking. We are still working on writing the documentations and cleaning up the code, but the following parts are sufficient for you to replicate the results in our paper. For more variants of the model, we have already moved all of our code onto the dev branch, so please feel free to check it out if you really need to delve deep recently. We will try our best to get everything ready as soon as possible.

If you find our paper or code useful for you, please consider cite us by:

@article{pang2021simpletrack,
  title={SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking},
  author={Pang, Ziqi and Li, Zhichao and Wang, Naiyan},
  journal={arXiv preprint arXiv:2111.09621},
  year={2021}
}

• Accelerating the code, make the IoU/GIoU computation parallel.
• Add documentation for codebase.

SimpleTrack requires python>=3.6 and the packages of pip install -r requirements.txt. For the experiments on Waymo Open Dataset, please install the devkit following the instructions at waymo open dataset devkit.

We implement the SimpleTrack algorithm as a library mot_3d. Please run pip install -e ./ to install it locally.

We provide a demo based on the first sequence with ID 10203656353524179475_7625_000_7645_000 from the validation set of Waymo Open Dataset and the detection from CenterPoint. (We are thankful and hope that we are not violating any terms here.)

First, download the demo_data and extract it locally. It contains the necessary information and is already preprocessed according to our preprocessing programs. To run the demo, please run the following command. It will provide interactive visualization with matplotlib.pyplt. Therefore, it is recommended to run this demo locally.

python tools/demo.py \
    --name demo \
    --det_name cp \
    --obj_type vehicle \
    --config_path configs/waymo_configs/vc_kf_giou.yaml \
    --data_folder ./demo_data/ \
    --visualize

An example output for the visualization is the following figure.

In the visualization, the red bounding boxes are the output tracking results with their IDs. The blue ones are the tracking results that are not output due to low confidence score. The green ones are the detection bounding boxes with scores. The black ones are the ground truth bounding boxes.

The most important function is tracker.frame_mot(). An object of MOTModel iteratively digests the information from each frame FrameData and infers the tracking result on each frame.

Refer to the documentation of Waymo Open Dataset Inference and nuScenes Inference. Important: please rigorously follow the config file path and instructions in the documentation to reproduce the results.

The detailed metrics and files are at Dropbox Link, Waymo Open Dataset in Dropbox link, nuScenes in Dropbox Link.

For the metrics on test set, please refer to our paper or the leaderboard.

To enable the better usages of our mot_3d library, we provide a list useful documentations, and will add more in the future.

• Read and Use the Configurations. We explain how to specify the behaviors of trackers in this documentation, such as two-stage association, the thresholds for association, etc.
• Format of Output. We explain the output format for the APIs in SimpleTrack, so that you may directly use the functions provided. (in progress)
• Visualization with mot_3d (in progress)
• Structure of mot_3d (in progress)