A Real Time Video Summarization for YouTube Videos and Evaluation of Computational Algorithms for their Time and Storage Reduction

Theaim of creating video summarization is for gathering huge video data and makes important points to be highlighted. Focus of this view is to avail the complete content of data for any particular video can be easy and clarity of indexing video. In recent days people use internet to surf and watch videos, images, play games, shows and many more activities. But it is highly impossible to go through each and every show or video because it can consume more time and data. Instead, providing highlights of any such shows or game videos then it will be helpful to go through and decide about that video. Also we can provide trailer part of any news/movie videos which can yield to make judgement of those incidents. We propose an interesting principle for highlighting videos mostly they can be online. These online videos can be shortened and summarized the huge video into smaller parts. In order to achieve this we use feature extracting algorithms called the gradient and optical flow histograms (HOG & HOF). In order to enhance the efficiency of the method several optimization techniques are also being implemented