Real-time highlight detection in baseball video for TVs with time-shift function

Time-shift is a crucial function of interactive televisions as like DVR and Internet TV broadcasting services. Automatic important event detection allows users utilize time- shift function conveniently. In this paper, we propose a method to extract important events in baseball videos. In the proposed method, we first detect play scenes and audio events separately from video and audio tracks. For robust play scene extraction, we proposed off-line learning model having local adaptation based on ongoing analyzed video. And we implemented the audio event detection with a SVM-based classifier. Final important events are determined by a combination of each audio-visual detection results in real time. We evaluated our method with a baseball database of Korean and Major League games. Experimental results show that the implemented system runs in real time and achieves a remarkable performance of 0.85 recall and 0.97 precision rates.