Video summarization using simple action patterns

Video summarization, which has a tremendous usage area that spreads from information retrieval to data compression, plays a crucial role in the multimedia understanding. In recent years, with the explosion of the number of videos and their area of use, video summarization became a must to signify. Therefore, this work introduces a novel approach for the summarization problem which is based on human movement understanding. Proposed system presents efficient video knowledge extraction, especially for surveillance cases. Human centric videos are analyzed with histogram of oriented gradients as feature extractor and optical flow as motion descriptor. Above these, a template matching algorithm implemented in a shrinkable and stretchable manner to search for periodicity and thereby extract patterns. Summarization is built up on the validation of these extracted patterns with a correlation based search-through subsystem.