Official implementation for TCSVT paper: “One-shot Multiple Object Tracking with Robust ID Preservation”
PIDMOT is built upon codebase of FairMOT. We use python 3.7 and pytorch >= 1.2.0
Step1. Install PIDMOT
git clone https://github.com/Kroery/PIDMOT.git
cd PIDMOT
pip3 install -r requirements.txt
Step2. Install DCNv2. We use DCNv2 in our backbone network and more details can be found in their repo.
git clone https://github.com/CharlesShang/DCNv2
cd DCNv2
./make.sh
- Download the training data
- Change the dataset root directory 'root' in src/lib/cfg/data.json and 'data_dir' in src/lib/opts.py
- Pretrain on MOTSynth and finetuned by CrowdHuman:
sh experiments/motsynth_saca_idm_clip.sh
sh experiments/crowdhuman_motsynth_saca_idm_clip.sh
- Train on MOT17:
sh experiments/mix_mot17_ch60_synth_saca_idm_clip.sh
- Train on MOT20:
sh experiments/mix_mot20_ch60_synth_saca_idm_clip.sh
- Tracking on MOT17 test set:
cd src
python track.py mot --arch dlaSACAidm_34 --load_model $model_path$ --test_mot17 True --match_thres 0.4 --conf_thres 0.25
- Tracking on MOT20 test set:
cd src
python track.py mot --arch dlaSACAidm_34 --load_model $model_path$ --test_mot20 True --match_thres 0.4 --conf_thres 0.25
@ARTICLE{10342840,
author={Lv, Weiyi and Zhang, Ning and Zhang, Junjie and Zeng, Dan},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={One-shot Multiple Object Tracking with Robust ID Preservation},
year={2023},
volume={},
number={},
pages={1-1},
doi={10.1109/TCSVT.2023.3339609}}