A Novel Event-Oriented Segment-of-Interest Discovery Method for Surveillance Video
During recent years, the quick development of computer techniques has witnessed the ever-increasing surveillance video data, which essentially pose great challenge on the data storage, management, analysis and even retrieval. Considering that most of the high volume of data is with no interest, we mainly investigate the problem of effectively and efficiently discovering segments-of-interest (SoI) in this paper. To do so, we propose a novel event-oriented Sol discovery method in two steps: first, we represent an event by modeling pixels' change in temporal-spatial space, aiming to unify both the inter-frames and frames-background changes; second, with the benefit of unsupervised learning, the prototype-event models could be learned from these detected events and in turn exploited to measure the interest factor of each prototype-event. The experiment results demonstrate that the proposed method precisely discriminate different events and effectively discover Sols.