부산대학교 도서관

Recently, more and more surveillance cameras are widely installed in public place to monitor people's behavior for various applications. Since surveillance videos are very long, we need a lot of time to scan a video in order to find a specific target. A traditional approach is to use the fast-forward method to saving the scanning time, while this method can shorten the video playing time, but it will also accelerate the movement speed of the targets, and may leads the targets hard to be seen clearly even be ignored. In this thesis, we propose a temporal-spatial quick browsing system for surveillance video to solve this problem. The user can not only fast browse surveillance videos but also clearly look at the target while retrieving targets. Our basic idea is first to extract all moving objects that carry the most significant information in a surveillance video, and then to rearrange their position on the time-axis of the video to short it. In addition, we try to preserve all the essential activities appearing in the original surveillance video. We also design a searching interface that can help the user to find a specific target and track its trajectory in surveillance videos of a camera network. The user can select a target in the compact video associate with a camera view. Then, our system automatically estimates its position in the camera network. If the user find any error occur, a new browsing process can be triggered to help user to track the target correct.