Multiview abnormal video synopsis in real-time

Abstract

The proliferation of surveillance cameras has resulted in an exponential growth in video data, which has created several challenges for video data analysis, extraction, and storage. Video synopsis is an efficient methodology for analyzing and storing video data because it creates short videos. However, existing video synopsis methods are inappropriate for abnormal behavior situations (e.g., constructing a video synopsis of only abnormal frames emphasizing gun and knife characteristics to predict violent activity). As most existing video synopsis methods rely on the foreground and multiple objects tracking preprocessing, they perform poorly in real-time scenarios, particularly in crowded situations. We proposed a video synopsis approach suitable for multiview cameras to mitigate this problem; it can also be applied to radically diverse crowdedness. A considerable difference exists between the proposed method and existing approaches because it has several unique properties. We first detected abnormal segments from a given video and then tracked and extracted the frames. We can extract the required meaningful data by applying this method, which increases the synopsis generation performance. Second, we created a summary of all the videos. Finally, a stitching algorithm was used to create the synopsis.