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https://github.com/HYUNJS/SGT
[WACV-2023] Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
https://github.com/HYUNJS/SGT
graph-neural-network joint-detection-and-tracking multi-object-tracking real-time
Last synced: 3 months ago
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[WACV-2023] Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
- Host: GitHub
- URL: https://github.com/HYUNJS/SGT
- Owner: HYUNJS
- Created: 2021-10-17T17:12:19.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-14T13:12:55.000Z (over 1 year ago)
- Last Synced: 2024-05-22T07:36:55.052Z (6 months ago)
- Topics: graph-neural-network, joint-detection-and-tracking, multi-object-tracking, real-time
- Language: Python
- Homepage:
- Size: 220 KB
- Stars: 54
- Watchers: 6
- Forks: 4
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-multiple-object-tracking - [code
README
# Sparse Graph Tracker (SGT)
[](https://paperswithcode.com/sota/multi-object-tracking-on-hieve?p=detection-recovery-in-online-multi-object)
[](https://paperswithcode.com/sota/multi-object-tracking-on-mot16?p=detection-recovery-in-online-multi-object)
[](https://paperswithcode.com/sota/multi-object-tracking-on-mot17?p=detection-recovery-in-online-multi-object)
[](https://paperswithcode.com/sota/multi-object-tracking-on-mot20-1?p=detection-recovery-in-online-multi-object)
Official code for Sparse Graph Tracker (SGT) based on the Detectron2 framework. Please feel free to leave an ISSUE or send me an email ([email protected]).
## 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
* Please refer [INSTALL.md](INSTALL.md) for the details
## Dataset Setup
* Please refer [DATASET.md](DATASET.md) for the details
## Model Zoo
- [download model weights](https://hkustconnect-my.sharepoint.com/:f:/g/personal/jhyunaa_connect_ust_hk/ErLZ6DG6CndHs-Lo12AxZKAB1kb4AJCh8adtnRAlXTNuzA?e=rKeboA)
* 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](https://hkustconnect-my.sharepoint.com/:u:/g/personal/jhyunaa_connect_ust_hk/EQW4mXblacdDtVc3uxaTsXYB2yqqUTQv9cwnBipAnpKblA?e=9pT2RO) |
| SGT | MOT17 + CrowdHuman | 60.8 | 76.4 | 72.8 | [model](https://hkustconnect-my.sharepoint.com/:u:/g/personal/jhyunaa_connect_ust_hk/EST0ZaRgqvlJoW2TGFpCUToBRUzGRkgXZQva32rypzWdZQ?e=OzCVgI) |
### MOT20
| Name | Dataset | HOTA | MOTA | IDF1| Download |
| :---: | :---: | :---: | :---: | :---: | :---: |
| SGT | MOT20 | 51.6 | 64.5 | 62.7 | [model](https://hkustconnect-my.sharepoint.com/:u:/g/personal/jhyunaa_connect_ust_hk/EVQks100QaRNp81QlSoQbwMBgncyxw-4cmE_eIrR3JPJoA?e=hQIHxF) |
| SGT | MOT20 + CrowdHuman | 57.0 | 72.8 | 70.6 | [model](https://hkustconnect-my.sharepoint.com/:u:/g/personal/jhyunaa_connect_ust_hk/EZWXTwJFbPNBs2d32RVAa84BYTjImlYMSsz-Fp4lt8aE6A?e=qiOoHw) |
### HiEve
| Name | Dataset | MOTA | IDF1 | Download |
| :---: | :---: | :---: | :---: | :---: |
| SGT | HiEve | 47.2 | 53.7 | [model](https://hkustconnect-my.sharepoint.com/:u:/g/personal/jhyunaa_connect_ust_hk/EVQks100QaRNp81QlSoQbwMBgncyxw-4cmE_eIrR3JPJoA?e=hQIHxF) |
## 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 <$DATA_ROOT> --register-data-name
python projects/Datasets/MOT/vis/vis_gt.py --data-root <$DATA_ROOT> --register-data-name --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
## Pipeline
## MOT Benchmark Results
## Ablation Experiment Results
## Visualization
## 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](https://github.com/facebookresearch/detectron2) which is released under the Apache 2.0 license and the detector [CenterNet](https://github.com/xingyizhou/CenterNet) which is released under the MIT license.
This codebase also provides Detectron2 version of [FairMOT](https://github.com/ifzhang/FairMOT) which is released under the MIT license.
## Citation
```BibTeX
@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}
}
```