Real-time detection of sport in MPEG-2 sequences using high-level AV-descriptors and SVM
We present a new approach for classifying MPEG-2 video sequences as dasiasportpsila or dasianon-sportpsila by analyzing new high-level audiovisual features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music video, news and sports are studied. Such applications have also been discussed in the context of MPEG-7. In our method the extracted features are logically combined by a support vector machine to produce a reliable detection. The results demonstrate a high identification rate of 98.5% based on a large balanced database of 100 representative video sequences gathered from free digital TV-broadcasting and world wide web.