This repository complements a research of Petr Pulc, Martin Holeňa et al. in extraction of human-comprehensible information from complex data.

In this particular case, we take a challenge of processing large amounts of multimedia content with high-resolution video and/or high frame rates.

First issue we came across, and that we tackle in this particular repository, is to enable real-time extraction of motion vectors from such large amounts of data with a low amount of false motion vectors. As a result, no additional motion vector filtering should be needed.

To this end, we propose a hierarchical approach that estimates a global homography on points of interest (features) in the top layer of Gaussian pyramid and uses this information during extraction of motion vectors in lower levels as an initial estimate of feature position in the incoming frame. Estimation given in lower levels is then based on exact matches from upper layers.

Please go to wiki for more technical information.

If you find herein presented approach helpful and use the algorithm in your own research, we kindly ask you for a reference to a following article:

Towards Real-time Motion Estimation in High-Definition Video Based on Points of InterestProceedings of the 2017 Federated Conference on Computer Science and Information Systems

DOI: 10.15439/2017f417

If you are interested how to apply our approach in your workflow, please contact us at: petrpulc@gmail.com

Prerequisites:

• OpenCV, developed against v. 3.4.1
  • CUDA support required for GPU acceleration
  • If CUDA is not available, merge the cpu branch to remove CUDA dependency

Otherwise, standard CMake procedure:

mkdir build
cd build
cmake ..
make

Binary (gpu_orb_tracker) is called with two arguments:

• Video source path
• Number of interest points detected by ORB (try 500 or more on decent hardware)

For a basic visualisation of the motion history, checkout the viz branch.

For more practical use, please note, that the most interesting data are hidden in the Motion object in the layers container, where the first OCTAVE layers (defined in settings.h) are objects of class PointLayer. These objects then contain a std::list of objects from class Feature. And these individual features contain in history a std::map of frame number and the position of the feature in cv::Point2f.

In case of any troubles, please don't hesitate to contact us.