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[WACV-2020] Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

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EpipolarScore

This repository is part of public implementation of our "Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency" paper. This repository contains Optical Flow & Epipolar Score Computation code. You can check the main repository here.

Installations:

This source code is based on MATLAB framework and tested on Ubuntu 16.04 with MATLAB 2016b.

Instructions:

1) Clone the repository
git clone https://github.com/mfaisal59/EpipolarScore.git
2) Download Dataset

Download and unpack the DAVIS 2016 dataset and as well as the evaluatio code from https://davischallenge.org/davis2016/code.html

3) Compute Optical Flow

The optical flow is based on Full Flow Method (https://cqf.io/fullflow/). To compute the optical flow for DAVIS Dataset, run the following script:

cd ./Full_Flow_Source_Code/
run davisBatch.m file
#modify the path to DAVIS dataset directory
4) Compute Epipolar Score

To compute the Epipolar Score, modify the paths in 'testDAVIS.m' file and run:

cd ./EpipolarScoreMain/
run testDAVIS.m script
#modify the path to DAVIS dataset, forward and backward optical flow directory
5) Convert Flow to X-Y Displacement Images
cd ./EpipolarScoreMain/
run flow2Displacement.m script
#modify the paths to DAVIS dataset, forward and backward optical flow directory
6) Generate Motion Images
cd ./EpipolarScoreMain/
run generateMotionImages.m script
#modify the paths to DAVIS dataset and Optical Flow directory

BIBTEX:

@article{DBLP:journals/corr/abs-1909-13258,
  author    = {Muhammad Faisal and
               Ijaz Akhter and
               Mohsen Ali and
               Richard I. Hartley},
  title     = {Exploiting Geometric Constraints on Dense Trajectories for Motion
               Saliency},
  journal   = {CoRR},
  volume    = {abs/1909.13258},
  year      = {2019},
  url       = {http://arxiv.org/abs/1909.13258}
}

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[WACV-2020] Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

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