Abstract
In this work, we have classified the frames of a broadcast soccer video into four classes, namely long shot, medium shot, close shot and logo frame. A two-stream deep neural network (DNN) model is proposed for the shot classification. Along with static image features, player attributes like count of the players in a frame, area, width and height of the players are used as features for the classification. The heterogeneous features are fed into the DNN model through a late fusion strategy. In addition to shot classification, we propose a model to detect replay within a soccer video. The logo frames are used to decide the temporal boundary of a replay segment. A majority class assignment strategy is employed to improve the accuracy of replay detection. The experimental results show that our method is at least 12% better than that of similar approaches.
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