Dynamic Bayesian network based event detection for soccer highlight extraction

In this paper, we propose a novel approach to event detection in soccer videos using dynamic Bayesian networks (DBNs). Based on such high level semantics, say, events, more meaningful soccer highlights are extracted. As a powerful statistical tool for time series signal processing, DBNs provide us with a feasible method to model sports events by combining contextual information and prior knowledge. In particular, we first develop a DBN model to interpret high-level events composed of low-level primitives in a soccer video. Then, we select a set of robust statistical features as observation input. Finally, the DBN model is gleaned to figure out the most likely series of events. The effectiveness of the proposed method has been demonstrated by our experiments.