Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2015.061233
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 12, 2015.
Abstract: For monitoring public domains, surveillance camera systems are used. Reviewing and processing any subsequences from large amount of raw video streams is time and space consuming. Many efficient approaches of video summarization were proposed to reduce the amount of irrelevant information. Most of these approaches do not take into consideration the illumination or lighting changes that cause noise in video sequences. In this work, video summarization algorithm for video streams has been proposed using Histogram of Oriented Gradient and Correlation coefficients techniques. This algorithm has been applied on the proposed multi-model dataset which is created by combining the original data and the dynamic synthetic data. This dynamic data is proposed using Random Number Generator function. Experiments on this dataset showed the effectiveness of the proposed algorithm compared with traditional dataset.
Nada Jasim Al-Musawi and Saad Talib Hasson, “Improving Video Streams Summarization Using Synthetic Noisy Video Data” International Journal of Advanced Computer Science and Applications(IJACSA), 6(12), 2015. http://dx.doi.org/10.14569/IJACSA.2015.061233