Official implementation of [2111.13672] Immortal Tracker: Tracklet Never Dies https://arxiv.org/abs/2111.13672
Our code is tested for Python 3.6.
To install required liabraries:
pip install -r requirements.txt
#Waymo Dataset
└── waymo
├── training (not required)
├── validation
├── testing
To extract timestamp infos/ego infos from .tfrecord files, run the following:
bash preparedata/waymo/waymo_preparedata.sh /<path to WOD>/waymo
Run the following to convert detection results into to .npz files. The detection results should be in official WOD submission format(.bin)
We recommand you to use CenterPoint(two-frame model for tracking) detection results for reproducing our results. Please follow https://github.com/tianweiy/CenterPoint or email its author for CenterPoint detection results.
bash preparedata/waymo/waymo_convert_detection.sh <path to detection results>/detection_result.bin cp
#you can also use other detections:
#bash preparedata/waymo/waymo_convert_detection.sh <path to detection results> <detection name>
Use the following command to start inferencing on WOD. The validation set is used by default.
python main_waymo.py --name immortal --det_name cp --config_path configs/waymo_configs/immortal.yaml --process 8
Follow https://github.com/waymo-research/waymo-open-dataset to build the evaluation tools and run the following command for evaluation:
#Convert the tracking results into .bin file
python evaluation/waymo/pred_bin.py --name immortal
#For evaluation
<path to WOD devkit>/bazel-bin/waymo_open_dataset/metrics/tools/compute_tracking_metrics_main mot_results/waymo/validation/immortal/bin/pred.bin <path to WOD tracking ground truth file>/validation_gt.bin
# For nuScenes Dataset
└── NUSCENES_DATASET_ROOT
├── samples
├── sweeps
├── maps
├── v1.0-trainval
├── v1.0-test
To extract timestamp infos/ego infos, run the following:
bash preparedata/nuscenes/nu_preparedata.sh <path to nuScenes>/nuscenes
Run the following to convert detection results into to .npz files. The detection results should be in official nuScenes submission format(.json)
We recommand you to use centerpoint(two-frame model for tracking) detection results for reproducing our results.
bash preparedata/nuscenes/nu_convert_detection.sh <path to detection results>/detection_result.json cp
#you can also use other detections:
#bash preparedata/nuscenes/nu_convert_detection.sh <path to detection results> <detection name>
Use the following command to start inferencing on nuScenes. The validation set is used by default.
python main_nuscenes.py --name immortal --det_name cp --config_path configs/nu_configs/immortal.yaml --process 8
Follow https://github.com/nutonomy/nuscenes-devkit to build the official evaluation tools for nuScenes. Run the following command for evaluation:
#To convert tracking results into .json format
bash evaluation/nuscenes/pipeline.sh immortal
#To evaluate
python <path to nuscenes-devkit>/nuscenes-devkit/python-sdk/nuscenes/eval/tracking/evaluate.py \
"./mot_results/nuscenes/validation_2hz/immortal/results/results.json" \
--output_dir "./mot_results/nuscenes/validation_2hz/immortal/results" \
--eval_set "val" \
--dataroot <path to nuScenes>/nuscenes
If this work is helpful for your research, please consider citing the following BibTeX entry.
@article{wang2021immortal,
title={Immortal tracker: Tracklet never dies},
author={Wang, Qitai and Chen, Yuntao and Pang, Ziqi and Wang, Naiyan and Zhang, Zhaoxiang},
journal={arXiv preprint arXiv:2111.13672},
year={2021}
}