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SLATracker

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Spatial-Attention Location-Aware Multi-Object Tracking

// repository documentation

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# SLATracker
This repository hosts our code for our paper ***Spatial-Attention Location-Aware Multi-Object Tracking***. (arxiv link not available now)  
The code will be released after the paper published.  
[Demo Bilibili](https://www.bilibili.com/video/BV1Ev411779v/)  
<div align='center'/><img src='https://github.com/JunnHan/SLATracker/blob/main/assets/MOT17-03.gif' /></div>

### Requirements
- Python3.6
- Pytorch 1.6.0, torchvision 0.7.0
- [detectron2](https://github.com/facebookresearch/detectron2)
- python-opencv
- py-motmetrics
- cython-bbox
### Installation
See [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md)
### Quick Start
#### Dataset Zoo
See [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md)  
**Note that we transform all used datasets to COCO format for training convenience.**  
**The pre-processing code will be uploaded later. Or you can download our transformed .json files from [GoogleDrive](https://drive.google.com/drive/folders/1bfaB9MRSyv_2AfmJYhu2WgiiMA2w8yje?usp=sharing)**
#### Training
`python3 train_net.py --config-file configs/faster_rcnn_R_50_FPN_1x.yaml`  
#### Visualization
A trained model is available at [GoogleDrive](https://drive.google.com/drive/folders/1bfaB9MRSyv_2AfmJYhu2WgiiMA2w8yje?usp=sharing) or [BaiduDisk](https://pan.baidu.com/s/126b0q2OI9Q9diDEvus3p4Q) (kw:sucy)  
`python3 demo/vis_track.py --config-file configs/faster_rcnn_R_50_FPN_1x.yaml --opts MODEL.WEIGHTS output/model_final.pth`
### MOTChallenge Results
[Official MOTChallenge website](https://motchallenge.net/)  
#### Public
Benchmark | MOTA | IDF1 | HOTA | MOTP | MT | ML | FP | FN | IDSw |
:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
2DMOT15 | 47.0 | 57.9 | 43.0 | 75.3 | 22.6 | 27.2 | 9044 | 22986 | 558 |
MOT16 | 60.6 | 59.5 | 46.8 | 78.0 | 24.2 | 29.1 | 5783 | 65469 | 643 |
MOT17 | 59.7 | 63.4 | 49.1 | 77.7 | 24.0 | 31.1 | 16644 | 209318 | 1647 |
  
**Note that we utilize the pre-processing method of Tracktor, not CenterTrack.**  
  
#### Private
Benchmark | MOTA | IDF1 | HOTA | MOTP | MT | ML | FP | FN | IDSw |
:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
2DMOT15 | 57.9 | 62.2 | 47.1 | 75.8 | 39.1 | 14.7 | 6973 | 18313 | 577 |
MOT16 | 72.0 | 69.6 | 54.7 | 77.9 | 37.3 | 20.9 | 7242 | 43147 | 740 |
MOT17 | 71.8 | 69.0 | 54.4 | 77.8 | 38.0 | 20.5 | 19077 | 137700 | 2493 |
### Acknowledgement
A large part of the code is borrowed from [Zhongdao/Towards-Realtime-MOT](https://github.com/Zhongdao/Towards-Realtime-MOT) and [DeanChan/HOIM-PyTorch](https://github.com/DeanChan/HOIM-PyTorch). Thanks for their wonderful works.

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