**Warning: the results can be slightly different due to Pytorch version and CUDA version.**

- Download pretrained models
all the pretrained models can be downloaded here:

[deepMOT-Tracktor.pth (google drive)](https://drive.google.com/file/d/181JzMrK5YyGecEZkKj-MPuAx2QLqSryO/view?usp=sharing) or

[deepMOT-Tracktor.pth (tencent cloud)](https://share.weiyun.com/5ZXIUL6)

- Enter the model path to parameter *obj_detect_weights* in the *test_tracktor/experiments/cfgs/tracktor_pub_reid.yaml* and *test_tracktor/experiments/cfgs/tracktor_private.yaml*

- Set the dataset name in the test_tracktor/experiments/cfgs/tracktor_pub_reid.yaml and test_tracktor/experiments/cfgs/tracktor_private.yaml:

For MOT17 (by default):
```
dataset: mot17_train_17
```

For MOT16 (images as the same as MOT17):
```
dataset: mot17_all_DPM_RAW16
```

- run tracking code
```
python test_tracktor/experiments/scripts/tst_tracktor_private.pytst_tracktor_pub_reid.py (public detections) or test_tracktor/experiments/scripts/tst_tracktor_private.py (private detections)
```

The results are saved by default under *test_tracktor/output/log/*, you can modify it by changing *output_dir* in the *test_tracktor/experiments/cfgs/tracktor_pub_reid.yaml* and *test_tracktor/experiments/cfgs/tracktor_private.yaml*.

- Visualization:

You can set write_images: True in the test_tracktor/experiments/cfgs/tracktor_pub_reid.yaml and test_tracktor/experiments/cfgs/tracktor_private.yaml to plot and save images.
By default, they will be saved inside *test_tracktor/output/log/* if *write_images: True*.

- [Setup](#environment-setup) your environment
- Go to the train_tracktor folder
- Download MOT Dataset can be downloaded here: [MOT17Det](https://motchallenge.net/data/MOT17Det.zip), [MOT16Labels](https://motchallenge.net/data/MOT16Labels.zip), [MOT16-det-dpm-raw](https://motchallenge.net/data/MOT16-det-dpm-raw.zip) and [MOT17Labels](https://motchallenge.net/data/MOT17Labels.zip).
- Unzip all the data by executing:
```
unzip -d MOT17Det MOT17Det.zip
unzip -d MOT16Labels MOT16Labels.zip
unzip -d 2DMOT2015 2DMOT2015.zip
unzip -d MOT16-det-dpm-raw MOT16-det-dpm-raw.zip
unzip -d MOT17Labels MOT17Labels.zip
```
- Enter the data path to *data_pth* in the *train_tracktor/experiments/cfgs/tracktor_full.yaml*

- Download the output folder containing the configurations and the model to be fine-tuned and DHN pre-trained model:

[output.zip (google drive)](https://drive.google.com/file/d/11Vu0bL-JaPQUWqHWv1VO89F0WJZWotUm/view?usp=sharing) or

[output.zip (tencent cloud)](https://share.weiyun.com/5nLyD1I)

- unzip the "output" folder and put it to *train_tracktor*.

- run training code
```
python train_tracktor/experiments/scripts/train_tracktor_full.py
```

The trained models are saved by default under *train_tracktor/output/log_full/* folder.

The tensorboard logs are saved by default under *deepmot/logs/train_log/* folder and you can visualize your training process by:
```
tensorboard --logdir=YourGitFolder/train_tracktor/output/log_full/
```
**Note:**
- you should install *tensorflow* (see [tensorflow installation](https://www.tensorflow.org/install/pip)) in order to visualize your training process.
```
pip install --upgrade tensorflow
```
### Train DHN
- Download the traindata (distance and ground-truth matrices calculated from MOT datasets):

[DHN data (google drive)](https://drive.google.com/file/d/1ICCm6tH_AgPSLzD3qac-6sYOvTIwwTNW/view?usp=sharing) or

[DHN data (tencent cloud)](https://share.weiyun.com/5OKPHxJ)

- unzip DHN_data and put the *DHN_data* folder to *train_DHN/*
- Run:
```
python train_DHN/train_DHN.py --is_cuda --bidirectional
```

for more parameter details please run:
```
python train_DHN/train_DHN.py -h
```
By default the trained models are saved into *train_DHN/output/DHN/* and log files in *train_DHN/log/*

your can visualize the training via tensorboard:
```
tensorboard --logdir=YourGitFolder/train_DHN/log/
```
**Note:**
- you should install *tensorflow* (see [tensorflow installation](https://www.tensorflow.org/install/pip)) in order to visualize your training process.
```
pip install --upgrade tensorflow
```

### Evaluation
You can run *test_tracktor/experiments/scripts/evaluate.py* to evaluate your tracker's performance.
- fill the list *predt_pth* in the code with the folder where the results (.txt files) are saved.
- make sure the data path is correctly set.
- then run
```
python test_tracktor/experiments/scripts/evaluate.py
```

### Results
MOT17 public detections:

| dataset | MOTA | MOTP | FN | FP | IDsW | Total Nb. Objs |
|-----------|----------|----------|--------|-------|------|----------------|
| train | 62.5% | 91.7% | 124786 | 887 | 798 | 336891 |
| test | 53.7% | 77.2% | 247447 | 11731 | 1947 | 564228 |

MOT16 public detections:

| dataset | MOTA | MOTP | FN | FP | IDsW | Total Nb. Objs |
|-----------|----------|----------|--------|-------|------|----------------|
| train | 58.8% | 92.2% | 44711 | 538 | 229 | 110407 |
| test | 54.8% | 77.5% | 78765 | 2955 | 645 | 182326 |

MOT16/17 private detections:

| dataset | MOTA | MOTP | FN | FP | IDsW | Total Nb. Objs |
|-----------|----------|----------|--------|-------|------|----------------|
| train | 70.0% | 91.3% | 32513 | 552 | 677 | 112297 |

**Note:**
- the results can be slightly different depending on the running environment.

**Single Object Tracker**: [**SiamRPN**](https://github.com/foolwood/DaSiamRPN), [**Tracktor**](https://github.com/phil-bergmann/tracking_wo_bnw/tree/iccv_19), [**Faster-RCNN pytorch implementation**](https://github.com/jwyang/faster-rcnn.pytorch/).
```
@inproceedings{Zhu_2018_ECCV,
title={Distractor-aware Siamese Networks for Visual Object Tracking},
author={Zhu, Zheng and Wang, Qiang and Bo, Li and Wu, Wei and Yan, Junjie and Hu, Weiming},
booktitle={European Conference on Computer Vision},
year={2018}
}

@InProceedings{Li_2018_CVPR,
title = {High Performance Visual Tracking With Siamese Region Proposal Network},
author = {Li, Bo and Yan, Junjie and Wu, Wei and Zhu, Zheng and Hu, Xiaolin},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018}
}

@InProceedings{tracktor_2019_ICCV,
author = {Bergmann, Philipp and Meinhardt, Tim and Leal{-}Taix{\'{e}}}, Laura},
title = {Tracking Without Bells and Whistles},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}}

@inproceedings{10.5555/2969239.2969250,
author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
title = {Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
year = {2015},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1},
pages = {91–99},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS’15}
}
```
**MOT Metrics in Python**: [**py-motmetrics**](https://github.com/cheind/py-motmetrics)

**Appearance Features Extractor**: [**DAN**](https://github.com/shijieS/SST)

```
@article{sun2018deep,
title={Deep Affinity Network for Multiple Object Tracking},
author={Sun, ShiJie and Akhtar, Naveed and Song, HuanSheng and Mian, Ajmal and Shah, Mubarak},
journal={arXiv preprint arXiv:1810.11780},
year={2018}
}
```
Training and testing Data from:

**MOT Challenge**: [**motchallenge**](https://motchallenge.net/data)
```
@article{MOT16,
title = {{MOT}16: {A} Benchmark for Multi-Object Tracking},
shorttitle = {MOT16},
url = {http://arxiv.org/abs/1603.00831},
journal = {arXiv:1603.00831 [cs]},
author = {Milan, A. and Leal-Taix\'{e}, L. and Reid, I. and Roth, S. and Schindler, K.},
month = mar,
year = {2016},
note = {arXiv: 1603.00831},
keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
```