Authors : Mehdi Miah, Justine Pepin, Nicolas Saunier & Guillaume-Alexandre Bilodeau
Polytechnique Montréal - 2020
[Project page] [arXiv paper] [ICPR 2020 paper]
Rank visual features for multiple object tracking focused on urban scenes
Linux and Windows supported. Python 3.6, Pytorch 1.4, CUDA 10.0
cd src
git clone https://github.com/KaiyangZhou/deep-person-reid.git #(torchreid 1.2.7)
mv deep-person-reid/ deep_person_reid/
pip install efficientnet_pytorch
git clone https://github.com/cw1204772/AIC2018_iamai.git
You can download the following datasets :
- WildTrack : https://www.epfl.ch/labs/cvlab/data/data-wildtrack/
- MOT17 : https://motchallenge.net/data/MOT17/
- DETRAC : http://detrac-db.rit.albany.edu/
- UAVDT : https://sites.google.com/site/daviddo0323/projects/uavdt
Change the path to data in the file ./src/dataset.py.
The weights for VGG-19, ResNet-18, DenseNet-121 come from Pytorch.
The weights for Efficient-B0 come from efficientnet_pytorch (automatically downloaded).
The weights for pedestrian ReID come from deep-person-reid
The weights for vehicles ReID come from AIC2018_iamai
Expand tree structure
.
├─── doc
├─── results
│ ├─── figure
│ ├─── logs
├─── script
│ ├─── run_DETRAC.sh
│ ├─── run_MOT17.sh
│ ├─── run_UAVDT.sh
│ ├─── run_WildTrack.sh
├─── src
│ ├─── affinity.py
│ ├─── analysis_rank.py
│ ├─── analysis_size.py
│ ├─── appearances.py
│ ├─── dataset.py
│ ├─── main.py
│ ├─── utils.py
│ ├─── AIC2018_iamai
│ ├─── deep_person_reid
├─── weights
│ ├─── model_880_base.ckpt
│ ├─── osnet_ain_x1_0_market1501_256x128_amsgrad_ep100_lr0.0015_coslr_b64_fb10_softmax_labsmth_flip_jitter.pthCompute the mean average precision on a specific scene with a chosen features and sigma :
python main.py --dataset=DETRAC --scene=20011 --feature=resnet18 --sigma=10
Compute the mean average precision on a whole dataset :
cd src ; bash ../script/run_DETRAC.sh
Analyze the ranking of features on a specific dataset :
python analysis_rank.py --dataset=DETRAC
Analyze effect of the size on features on a specific dataset :
python analysis_size.py --dataset=DETRAC
If you refer to this work, please cite :
@inproceedings{miah2020empirical,
title = {An {Empirical} {Analysis} of {Visual} {Features} for {Multiple} {Object} {Tracking} in {Urban} {Scenes}},
author = {Miah, Mehdi and Pepin, Justine and Saunier, Nicolas and Bilodeau, Guillaume-Alexandre},
booktitle = {International {Conference} on {Pattern} {Recognition} ({ICPR})},
year = {2020}
}We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [CRDPJ 528786 - 18], [DG 2017-06115] and the support of Arcturus Networks.