Official implementation of the WOFT tracker from the paper
Jonáš Šerých, Jiří Matas: ”Planar Object Tracking via Weighted Optical Flow”, WACV 2023
Please cite our paper, if you use any of this.
@inproceedings{serych2023planar,
title={Planar Object Tracking via Weighted Optical Flow},
author={{\v{S}}er{\'{y}}ch, Jon{\'{a}}{\v{s}} and Matas, Ji{\v{r}}{\'{i}}},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={1593--1602},
year={2023}
}
This work is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International license.
The pytracking/external/RAFT directory contains a slightly modified copy of RAFT, which is licensed under BSD-3-Clause license, except for the pytracking/external/RAFT/raft_core/weighted_raft.py file, which is again licensed under the Attribution-NonCommercial-ShareAlike 4.0 International.
The demo directory includes samples from the POT-210 dataset.
python -m pip install virtualenv
python -m virtualenv envs/WOFT
source envs/WOFT/bin/activate
python -m pip install tqdm einops opencv-python kornia==0.5.11 ipdb pandas matplotlib scipyFor the liteflownet experiment, install the following
python -m pip install torch==1.8.1 torchvision==0.9.1 tqdm einops opencv-python kornia==0.5.11 ipdb pandas matplotlib scipy openmim==0.2.1
mim install mmcv-full==1.6.1
# we use a very slightly modified / patched version of mmflow (included as submodule):
git submodule update --init --recursive
cd pytracking/external/mmflow
python -m pip install -v -e .
mim download mmflow --config liteflownet2_ft_4x1_600k_sintel_kitti_320x768for video demo, run:
python WOFT_demo.py demo/V25_2/
# or
python WOFT_demo.py demo/V24_7/Feel free to try other configs, like for example:
python WOFT_demo.py demo/V25_2/ --config pytracking/configs/WOFT_downscale_3x.pyThis work was supported by Toyota Motor Europe, by CTU student grant SGS20/171/OHK3/3T/13, and by the Research Center for Informatics project CZ.02.1.01/0.0/0.0/16_019/0000765 funded by OP VVV.