Order-Preserving Condensation of Moving Objects in Surveillance Videos

Abstract

Vision-based detection of illegal or accidental activities in urban traffic has attracted great interest. Since state-of-the-art online automated detection algorithms are far from perfect, much research effort on offline video surveillance has been made to prevent police or security staff from observing all recorded video frames unnecessarily. To solve the problem, this study focuses on video condensation, which provides fast monitoring of moving objects in a long duration of surveillance videos. Considering the computational complexity and the condensation ratio as the two main criteria for efficient video condensation, we propose a video condensation algorithm, which consists of the following: 1) initial condensation by discarding frames of nonmoving objects; 2) intra-GoFM (group of frames with moving objects) condensation; and 3) inter-GoFM condensation. In the intra-GoFM and inter-GoFM condensation, spatiotemporal static pixels within each GoFM and temporal static pixels between two consecutive GoFMs are dropped to shorten the temporal distances between consecutive moving objects. Experimental results show that our video condensation saves a significant amount of computational loads compared with the previous methods without sacrificing the condensation ratio and visual quality.