Generation of sports highlights using motion activity in combination with a common audio feature extraction framework

In our past work we have used temporal patterns of motion activity to extract sports highlights. We have also used audio classification based approaches to develop a common audio-based platform for feature extraction that works across three different sports. In this paper, we combine the two aforementioned complementary approaches so as to get higher accuracy. We propose a framework for mining the semantic audio-visual labels in order to detect "interesting" events. Our results show that the proposed techniques work well across our three sports of interest, soccer, golf and baseball.