Identifying sports videos using replay, text, and camera motion features

Vikrant Kobla; Daniel DeMenthon; David Scott Doermann

doi:10.1117/12.373565

23 December 1999 Identifying sports videos using replay, text, and camera motion features

Vikrant Kobla, Daniel DeMenthon, David Scott Doermann

Proceedings Volume 3972, Storage and Retrieval for Media Databases 2000; (1999) https://doi.org/10.1117/12.373565
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

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.

Citation Download Citation

Vikrant Kobla, Daniel DeMenthon, and 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); https://doi.org/10.1117/12.373565

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