Abstract
Due to an increasing demand for video surveillance, there is an explosive growth of surveillance videos, which causes a big challenge in video storage, browsing, and retrieval. The video synopsis technique is thus developed to extract and rearrange the moving objects so as to handle the massive video browsing challenge. However, the traditional video synopsis (TVS) method only considers the processing videos captured by a single camera, ignoring object interactions in multicamera videos. To address this issue, we propose a novel multicamera joint video synopsis (JVS) algorithm for multicamera surveillance videos. First, a key time stamp (KTS) selection method is designed to find an object’s appearing, merging, splitting, and disappearing moments in the frame sequence, called tube, of that object. Second, tubes are rearranged by minimizing a global energy function that involves the overall camera views. Compared with the energy function used in TVS, the proposed global energy function considers the chronological orders of tubes not only in the same camera view but also among different camera views. Moreover, the chronological disorder cost term is formulated based on the KTS labels, and improved by considering the visual similarity between two tubes. Finally, the multicamera synopsis videos are separately generated by stitching together the globally rearranged tubes and background images of the same camera view. Extensive experiments show that the proposed JVS method is better than the traditional single-camera video synopsis method in preserving the chronological orders of moving objects among multicamera synopsis videos.
Published Version
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