News: We release the code for training and testing DeepMOT-Tracktor and the code for training DHN. Please visit: https://gitlab.inria.fr/robotlearn/deepmot
How To Train Your Deep Multi-Object Tracker
Yihong Xu, Aljosa Osep, Yutong Ban, Radu Horaud, Laura Leal-Taixé, Xavier Alameda-Pineda
[Paper]
If you find this code useful, please star the project and consider citing:
@inproceedings{xu2020train,
title={How To Train Your Deep Multi-Object Tracker},
author={Xu, Yihong and Osep, Aljosa and Ban, Yutong and Horaud, Radu and Leal-Taix{\'e}, Laura and Alameda-Pineda, Xavier},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={6787--6796},
year={2020}
}
This code has been tested on Ubuntu 16.04, Python 3.6, Pytorch=0.4.1, CUDA 9.2, 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 git@gitlab.inria.fr:yixu/deepmot.git && cd deepmot
Option 1:
- Follow the installation instructions in Tracktor.
Option 2 (recommended):
we provide a Singularity image with all packages pre-installed (similar to Docker) for training and testing.
- Open a terminal
- Install Singularity > 3.0 package:
https://sylabs.io/guides/3.3/user-guide/installation.html#install-on-linux - Download the Singularity image:
tracker.sif (google drive) or
tracker.sif (tencent cloud) - Open a new terminal
- Launch a Singularity image
singularity shell --nv --bind yourLocalPath:yourPathInsideImage tracker.sif- -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 the local Nvidia driver.
-
Setup your environment
-
Go to the test_tracktor folder
-
Download MOT data Dataset can be downloaded here: MOT17Det, MOT16Labels, MOT16-det-dpm-raw and MOT17Labels . 2. 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 test_tracktor/experiments/cfgs/tracktor_pub_reid.yaml and test_tracktor/experiments/cfgs/tracktor_private.yaml
-
Download pretrained models all the pretrained models can be downloaded here:
deepMOT-Tracktor.pth (google drive) or
deepMOT-Tracktor.pth (tencent cloud) -
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 your environment
-
Go to the train_tracktor folder
-
Download MOT Dataset can be downloaded here: MOT17Det, MOT16Labels, MOT16-det-dpm-raw and MOT17Labels.
-
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) or
output.zip (tencent cloud) -
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) in order to visualize your training process.
pip install --upgrade tensorflow
-
Download the traindata (distance and ground-truth matrices calculated from MOT datasets):
DHN data (google drive) or
DHN data (tencent cloud) -
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) in order to visualize your training process.
pip install --upgrade tensorflow
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
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.
Some code is modified and network pre-trained weights are obtained from the following repositories:
Single Object Tracker: SiamRPN, Tracktor, Faster-RCNN pytorch implementation.
@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
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}
}