This is the official implementation with training code for the paper:
DeepMOT: A Differentiable Framework for Training Multiple Object Trackers
Yihong Xu, Yutong Ban, Xavier Alameda-Pineda, Radu Horaud
[Paper]
If you find this code useful, please star the project and consider citing:
@article{xu2019deepmot,
title={DeepMOT: A Differentiable Framework for Training Multiple Object Trackers},
author={Xu, Yihong and Ban, Yutong and Alameda-Pineda, Xavier and Horaud, Radu},
journal={arXiv preprint arXiv:1906.06618},
year={2019}
}
This code has been tested on Ubuntu 16.04, Python 3.6, Pytorch> 0.4.1, CUDA 9.0 and CUDA 10.0, GTX 1080Ti, Titan X and RTX Titan GPUs.
warning: the results can be slightly different due to Pytorch version and CUDA version.
- Clone the repository
git clone https://github.com/yihongXU/deepMOT.git && cd deepmot
Option 1:
- Setup python environment
conda create -n deepmot python=3.6
source activate deepmot
pip install -r requirements.txt
Option 2: we offer Singularity images (similar to Docker) for training and testing.
- Open a terminal
- Install singularity
sudo apt-get install -y singularity-container
- Download a Singularity image and put it to deepmot/SingularityImages
pytorch_cuda90_cudnn7.simg(google drive)
pytorch1-1-cuda100-cudnn75.simg(google drive)
pytorch_cuda90_cudnn7.simg(tencent cloud)
pytorch1-1-cuda100-cudnn75.simg(tencent cloud) - Open a new terminal
- Launch a Singularity image
cd deepmot
singularity shell --nv --bind yourLocalPath:yourPathInsideImage ./SingularityImages/pytorch1-1-cuda100-cudnn75.simg- -bind: to link a singularity path with a local path. By doing this, you can find data from local PC inside Singularity image;
- -nv: use local Nvidia driver.
We provide code for performing tracking with our pre-trained models on MOT Challenge dataset. The code outputs txt files for MOT Challenge submissions, they can also be used for plotting bounding boxes and visualization.
-
Setup your environment
-
Download MOT data Dataset can be downloaded here: e.g. MOT17
-
Put MOT dataset into deepmot/data/ and it should have the following structure:
mot
|-------train
| |
| |---video_folder1
| | |---det
| | |---gt
| | |---img1
| |
| |---video_folder2
...
|-------test
| |
| |---video_folder1
| | |---det
| | |---img1
...
- Download pretrained models
all the pretrained models can be downloaded here:
pretrained models(google drive) or
pretrained models(tencent cloud)
-Put all pre-trained models to deepmot/pretrained/
- run tracking code
python tracking_on_mot.py
for more details about parameters, do:
python tracking_on_mot.py -h
The results are save by default under deepmot/saved_results/txts/test_folder/.
- Visualization After finishing tracking, you can visualize your results by plotting bounding box to images.
python plot_results.py
the results are save by default under deepmot/saved_results/imgs/test_folder
Note:
- we clean the detections with nms and threshold of detection scores. They are saved into numpy array in the folder deepmot/clean_detections, if you have trouble opening them, try to add allow_pickle=True to np.load() function.
We provide codes for evaluting tracking results in terms of MOTP and MOTA:
python evaluation.py --txts_path=yourTxTfilesFolder
MOT17:
| dataset | MOTA | MOTP | FN | FP | IDsW | Total Nb. Objs |
|---|---|---|---|---|---|---|
| train | 49.249% | 82.812% | 149575 | 19807 | 1592 | 336891 |
| test | 48.500% | 76.900% | 262765 | 24544 | 3160 | 564228 |
Note:
- the results are better than reported in the paper because we add Camera Motion Compensation to deal with moving camera videos.
- the results can be slightly different depending on the running environment.
-
Setup your environment
-
Download MOT data Dataset can be downloaded here: e.g. MOT17
-
Put MOT dataset into deepmot/data and it should have the following structure:
mot
|-------train
| |
| |---video_folder1
| | |---det
| | |---gt
| | |---img1
| |
| |---video_folder2
...
|-------test
| |
| |---video_folder1
| | |---det
| | |---img1
...
- Download pretrained SOT model SiamRPNVOT.model
SiamRPNVOT.model (from SiamRPN, Li et al., see Acknowledgement):
SiamRPNVOT.model(google drive) or
SiamRPNVOT.model(tencent cloud)
-Put SiamRPNVOT.model to deepmot/pretrained/ folder
- run training code
python train_mot.py
for more details about parameters, do:
python train_mot.py -h
The trained models are save by default under deepmot/saved_models/ folder.
The tensorboard logs are saved by default under deepmot/logs/train_log/ folder and you can visualize your training process by:
tensorboard --logdir=/mnt/beegfs/perception/yixu/opensource/deepMOT/logs/train_log
Note:
- you should install tensorflow (see tensorflow installation) in order to visualize your training process.
pip install --upgrade tensorflow
Some codes are modified and network pretrained weights are obtained from the following repositories:
Single Object Tracker: SiamRPN
@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}
}
MOT Metrics in Python: py-motmetrics
Appearance Features Extractor: DAN
@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
@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}
}