23 December 1999 Identifying sports videos using replay, text, and camera motion features
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Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vikrant Kobla, Vikrant Kobla, Daniel DeMenthon, Daniel DeMenthon, David Scott Doermann, David Scott Doermann, } "Identifying sports videos using replay, text, and camera motion features", Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373565; https://doi.org/10.1117/12.373565


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