Estimation and representation of accumulated motion characteristics for semantic event detection

In this paper, a motion-based approach for detecting high-level semantic events in video sequences is presented. Its main characteristic is its generic nature, i.e. it can be directly applied to any possible domain of concern without the need for domain-specific algorithmic modifications or adaptations. For realizing event detection, the examined video sequence is initially segmented into shots and for every resulting shot appropriate motion features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing the association of each shot with one of the high-level semantic events that are of interest in any given domain. Regarding the motion feature extraction procedure, a new representation for providing local-level motion information to HMMs is presented, while motion characteristics from previous frames are also exploited. Experimental results as well as comparative evaluation from the application of the proposed approach in the domain of news broadcast video are presented.