An adaptive neuro-fuzzy approach for semantic analysis of broadcast soccer video

This paper presents an approach for automatic annotation of soccer video based on the semantic events occurred inside it. The goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined patterns in event detection process. To achieve this goal, we propose a fuzzy inference system (FIS) implemented in the framework of an adaptive neural network which combines the self-learning capability of neural networks with explicit knowledge representation and precision of fuzzy based classification systems. This method provides the capability for fuzzy systems to learn information about a set of data in order to determine the parameters of membership functions (MFs) automatically and generate a set of fuzzy rules that best allow the FIS to track the input/output data. The proposed method is multimodal and employs statistical information from a set of audiovisual features that are organized in a hierarchical structure as input and produces semantic concepts corresponding to the occurred events. Experimental results conducted on a large set of soccer videos demonstrate the effectiveness of the proposed approach.