Video semantic analysis is formulated based on the low-level image features and the high-level knowledge which is encoded in abstract, nongeometric representations. This paper introduces a semantic analysis system based on Bayesian network (BN) and dynamic Bayesian network (DBN). It is validated in the particular domain of soccer game videos. Based on BN/DBN, it can identify the special events in soccer games such as goal event, corner kick event, penalty kick event, and card event. The video analyzer extracts the low-level evidences, whereas the semantic analyzer uses BN/DBN to interpret the high-level semantics. Different from previous shot-based semantic analysis approaches, the proposed semantic analysis is frame-based for each input frame, it provides the current semantics of the event nodes as well as the hidden nodes. Another contribution is that the BN and DBN are automatically generated by the training process instead of determined by ad hoc. The last contribution is that we introduce a so-called temporal intervening network to improve the accuracy of the semantics output.

This paper proposes a novel semantics-based content analysis system for reliable media highlight extraction using dynamic Bayesian network (DBN). It extracts the low-level evidences and then converts the input video to high-level semantic meaning. Specific domains contain rich spatial and temporal transitional structures that help the transformation process. We introduce a robust audio-visual low-level evidence extraction scheme, and develop the so-called temporal intervening network to improve the performance of our system. In experiments, we show that our system can detect soccer events such as goal event, corner kick event, penalty kick event, and card event effectively.