Affection arousal based highlight extraction for soccer video
Highlight extraction, as one of the key technologies in soccer video retrieval and summarization, has great academic and application value. According to the principle that the observers affection state would fluctuate with the evolution of game process when watching soccer match video, a novel highlight extraction approach based on the improved affection arousal model is proposed. Compared with the existing works, our main contributions include the following. A novel feature  shot intensity is exploited to replace the motion activity, which greatly improves the computational performance of affection arousal model. Another helpful feature  replay factor is designed and successfully fused into the affection arousal model. This makes the affection arousal model reflect the variation of the true match process more accurately. In addition, event temporal transition pattern (ETTP) in soccer video is utilized to detect highlights boundaries effectively combined with the affection arousal curve. Experiments conducted on real-world soccer game videos have demonstrated the efficiency and effectiveness of the proposed approach.
highlight extraction affection arousal model soccer video analysis event detection