Detection of the Periodicity of Human Actions for Efficient Video Summarization

Abstract

video surveillance and tracking systems have become an integral part of our daily life. As a consequence an enormous amount of data is being produced and recorded on a daily basis, which makes storage, analysis and information extraction extremely difficult. Some of the recent studies have especially focused on cost-effective yet efficient video summarization methods. Video summarization methods for surveillance systems primarily aim at detecting and evaluating human actions within the videos. Most of the time human actions do consist of periodic actions. In this study we propose a novel approach to video summarization, which is based on the detection of the periodicity of different actions and summarizing the video using this information. Our proposed method is based on the popular string match algorithm, longest common subsequence, to determine the shortest period of recurring human actions. The concatenation of the shortest periods then produces the summary video. Our test showed that proposed approach summarized original video by shortening at a ratio of 85 percent.