A Python Wrapper around the Optical Flow method developed during Cei Lui's PhD Thesis. Please visit his webpage (linked here) for more information. As of Sep 2nd, 2021 this method is ranked 197 on the KITTI 2015 leaderboard for Optical Flow.
The wrapper was made on a system running Ubuntu 20 LTS. The project depends on SWIG and OpenCV. To install SWIG:
sudo apt-get install -y swig
OpenCV installs are covered well online.
To install, please use the following instructions from the root directory
mkdir ./build
cd build
cmake ..
make
python ./pytests/test_optical_flow.py
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[2] T. Brox, A. Bruhn, N. Papenberg, and J.Weickert. High accuracy optical flow estimation based on a theory for warping. In European Conference on Computer Vision (ECCV), pages 25–36, 2004.
[3] A. Bruhn, J.Weickert and C. Schn¨orr. Lucas/Kanade meets Horn/Schunk: combining local and global optical flow methods. International Journal of Computer Vision (IJCV), 61(3):211–231, 2005.
[4] C. Liu, W. T. Freeman, E. H. Adelson and Y. Weiss. Human-assisted motion annotation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.
[5] C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. Massachusetts Institute of Technology. May 2009.