SPORTS VIDEO CLASSIFICATION IN CONTINUOUS TV BROADCASTS

This paper is focused on classification of video footages or continuous TV broadcasts by its content. The considered classification categories (topics) are either general (talk show, sport, movie, cartoon...) or more specific (summer and winter Olympic sports, e.g. cycling, tennis, archery, box...). At first, each frame of the video is classified separately. It is shown that the classification results are more accurate and robust when the per-frame results are filtered in time domain. The main part of the paper deals with selection of robust image features and classifiers. It is shown that simple feature extractors are surpassed by complex features based on convolutional neural networks.