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[WACV-2023] Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker

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Sparse Graph Tracker (SGT)

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Official code for Sparse Graph Tracker (SGT) based on the Detectron2 framework. Please feel free to leave an ISSUE or send me an email (jhyunaa@ust.hk).

News

  • (2022.10.11) Our paper is accepted WACV 2023! (arxiv paper will be updated soon)
  • (2022.10.06) Code and pretrained weights are released!

Installation

Dataset Setup

Model Zoo

  • Please modify the path of checkpoints in the config file based on your checkpoint directory

MOT17

Name Dataset HOTA MOTA IDF1 Download
SGT MOT17 58.2 73.2 70.2 model
SGT MOT17 + CrowdHuman 60.8 76.4 72.8 model

MOT20

Name Dataset HOTA MOTA IDF1 Download
SGT MOT20 51.6 64.5 62.7 model
SGT MOT20 + CrowdHuman 57.0 72.8 70.6 model

HiEve

Name Dataset MOTA IDF1 Download
SGT HiEve 47.2 53.7 model

How to run?

Train

python projects/SGT/train_net.py --config-file projects/SGT/configs/MOT17/sgt_dla34.yaml --data-dir /root/datasets --num-gpus 2 OUTPUT_DIR /root/sgt_output/mot17_val/dla34_mot17-CH

Inference

python projects/SGT/train_net.py --config-file projects/SGT/configs/MOT17/sgt_dla34.yaml --data-dir /root/datasets --num-gpus 1 --eval-only OUTPUT_DIR /root/sgt_output/mot17_test/dla34_mot17-CH

Visualization

## GT
python projects/Datasets/MOT/vis/vis_gt.py --data-root <$DATA_ROOT> --register-data-name <e.g., mot17_train> 
python projects/Datasets/MOT/vis/vis_gt.py --data-root <$DATA_ROOT> --register-data-name <e.g., mix_crowdhuman_train> --no-video-flag 


## model output
python projects/Datasets/MOT/vis/vis_seq_from_txt_result.py --data-root <$DATA_ROOT> --result-dir <$OUTPUT_DIR> --data-name {mot17, mot20, hieve, mot17_sub, mot20_sub} --tgt-split {val,test}

Motivation

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Pipeline

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MOT Benchmark Results

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Ablation Experiment Results

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Visualization

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License

Code of SGT is licensed under the CC-BY-NC 4.0 license and free for research and academic purpose. SGT is based on the framework Detectron2 which is released under the Apache 2.0 license and the detector CenterNet which is released under the MIT license. This codebase also provides Detectron2 version of FairMOT which is released under the MIT license.

Citation

@inproceedings{hyun2023detection,
  title={Detection recovery in online multi-object tracking with sparse graph tracker},
  author={Hyun, Jeongseok and Kang, Myunggu and Wee, Dongyoon and Yeung, Dit-Yan},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={4850--4859},
  year={2023}
}

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[WACV-2023] Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker

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