The code is built with CenterTrack. Thanks for their great work.
Please refer to INSTALL.md for installation instructions.
The data in the following structure:
DIVOTrack
└——————datasets
| └——————DIVO
| |——————images
| | └——————train
| | └——————test
| └——————labels_with_ids
| | └——————train
| | └——————test
| └——————annoatations
| | └——————train.json
| | └——————test.json
└——————${ROOT}
If your data does not has the "annotations" directory, please use the following command to generate
cd ${CenterTrack_ROOT}
cd src
python convert_divo_to_coco.py
The pre-trained model can download from Google Drive and put it into ./models/
cd ${CenterTrack_ROOT}
cd src
python main.py tracking --exp_id divo --dataset divo --pre_hm --ltrb_amodal --same_aug --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --gpus 0,1 --load_model ../models/crowdhuman.pth --num_epochs 30
or
sh ./experiments/divo_train.sh
Make sure the "exp_id" is the same as the training one, the model will be directly loaded from the corresponding dir "exp/tracking/"exp_id"/model_last.pth".
cd ${CenterTrack_ROOT}
cd src
python test.py tracking --exp_id divo --dataset divo --pre_hm --ltrb_amodal --track_thresh 0.4 --pre_thresh 0.5 --resume
or
sh ./experiments/divo_test.sh
The result will be saved to "exp/tracking/"exp_id"/result_divo
- Download our final model and put it into
./exp/tracking/"exp_id"/
- Change the directory name from "result_divo" to "centertrack"
- Make sure "centertrack" has the middle directory "data". (i.e. centertrack/data/circleRegion_Drone.txt instead of centertrack/circleRegion_Drone.txt)
- Copy your result_divo to DIVOTrack/TrackEval/data/trackers/mot_challenge/divo
- Go to DIVOTrack/TrackEval
- See the instrcution on TrackEval
CenterTrack is developed upon CenterNet. Both codebases are released under MIT License themselves. Some code of CenterNet are from third-parties with different licenses, please check the CenterNet repo for details. In addition, this repo uses py-motmetrics for MOT evaluation and nuscenes-devkit for nuScenes evaluation and preprocessing. See NOTICE for detail. Please note the licenses of each dataset. Most of the datasets we used in this project are under non-commercial licenses.