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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 video is initially segmented into shots and for every resulting shot appropriate motion features are extracted at fixed time intervals, thus forming a motion observation sequence. Then, Hidden Markov Models (HMMs) are employed for associating each shot with a semantic event based on its formed observation sequence. 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. The latter is based on the observation that motion information from previous frames can provide valuable cues for interpreting the semantics present in a particular frame. Experimental results as well as comparative evaluation from the application of the proposed approach in the domains of tennis and news broadcast video are presented.

Accumulated motion energy fields estimation and representation for semantic event detection