A new method for shot classification in soccer sports video based on SVM classifier

Sport video shot classification is a basic step in the sport video processing. For many purposes such as event detection and summarization, shot classification is needed for content filtering. In this paper, we present a new method for soccer video shot classification. At first, in-field and out-of-field frames are separated. In in-field frames three features based on number of connected components and shirt color percent in vertical and horizontal strips are extracted. The features are all new and showed excellent discrimination in the feature space. These features are given to SVM for classifying long, medium and close-up shots. One of the advantages of our method is that, close-ups can be detected in both in-field and out-of-field views. For detecting close-ups in out-of-field shots, the mean of shirt color in horizontal strips is used. Since the features are easy to extract and input frames are downsampled, the method works in real-time. The experimental results demonstrated the effectiveness of proposed method.