Rushes Video Parsing Using Video Sequence Alignment

In this paper, we propose a novel method inspired by the bio-informatics domain to parse a rushes video into scenes and takes. The Smith-Waterman algorithm provides an efficient way to compare sequences by comparing segments of all possible lengths and optimizing the similarity measure. We propose to adapt this method in order to detect repetitive sequences in rushes video. Based on the alignments found, we can parse the video into scenes and takes. By comparing takes together, we can select the most complete take in each scene. This method is evaluated on several rushes videos from the TRECVID BBC Rushes Summarization campaign.