Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.