Object-centered narratives for video surveillance
Wei Fu, Jinqiao Wang, Chaoyang Zhao, Hanqing Lu, Songde Ma · 2012
Effective video presentation and summarization techniques are critical for fast browsing of video content. In this paper, we propose a novel presentation approach to vividly depict the moving process of a specific object in a surveillance video, which aims at effectively summarizing video content by a static image named narrative. Firstly, the object of interest is extracted and segmented from the video to form a spatio-temporal object tube. Then three criteria are proposed to select the most representative objects from this tube. We formulate the object selecting process as an energy minimization problem, in which each energy term measures a corresponding criterion cost. We maximally preserve the changes of appearance and behavior while remove other redundant content as much as possible. Finally, the selected representative objects are stitched to the background image by Poisson editing. Experimental results show the promise of the proposed approach.