High-Performance Video Condensation System

Abstract

Video synopsis or condensation is a smart solution for fast video browsing and storage. However, most of the existing methods work offline, where two main phases are required. The first phase is to prepare tubes and background images. The second phase is to rearrange tubes and stitch them into backgrounds. However, with a long video sequence, the first phase is memory consuming for data storage, and the second phase is computationally expensive to rearrange all tubes simultaneously. To overcome these problems, we propose a high-performance video condensation system based on an online content-aware framework. The online framework transforms the optimization problem of tube rearrangement into a stepwise optimization problem. Therefore, it can condense video with much less memory and higher speed than the offline framework. With the aid of this transformation, the proposed system can process input videos and produce condensed videos simultaneously. Thus it is suitable for real-time endless surveillance videos. Meanwhile, the online mechanism allows users to directly visit the condensation video that has been generated. Moreover, the content-aware mechanism makes the proposed system able to automatically determine the duration of a condensed video. Finally, the proposed system uses Graphic Processing Unit (GPU) and multicore techniques to improve the speed. Extensive experiments that validate the high efficiency of the system are presented.