Improving Video Streams Summarization Using Synthetic Noisy Video Data

Abstract

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.

An Optimized Deep Learning Method for Video Summarization Based on the User Object of Interest

“…For video summarization, the shot boundary detection techniques [8][9][10][11][12][13][14][15] are widely known. Instead of concentrating on a single item, feature-based techniques [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] for VS provide a generalized video summary. These methods have trouble accurately recognizing the item, which prevents them from meeting the user's needs.…”