Identifying Sports Videos using Replay, Text and Camera Motion Features

Automated classification of digital video is emerging as an important piece of the puzzle in the design of contentmanagement 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 video, 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%.