In this paper, we propose a novel approach for detecting highlights in sports videos. The videos are temporally decomposed into a series of events based on an unsupervised event discovery and detection framework. The framework solely depends on easy-to-extract low-level visual features such as color histogram (CH) or histogram of oriented gradients (HOG), which can potentially be generalized to different sports. The unigram and bigram statistics of the detected events are then used to provide a compact representation of the video. The effectiveness of the proposed representation is demonstrated on cricket video classification: Highlight vs. Non-Highlight for individual video clips (7000 training and 7000 test instances). We achieve a low equal error rate of 12.1% using event statistics based on CH and HOG features.
['Hao Tang', 'Vivek Kwatra', 'Mehmet Emre Sargin', 'Ullas Gargi']
Detecting highlights in sports videos: Cricket as a test case