A knowledge-based approach to domain-specific compressed video analysis

A novel approach to domain-specific video analysis is proposed. The proposed approach is based on exploiting domain-specific knowledge in the form of an ontology to detect video objects corresponding to the semantic concepts defined in the ontology. The association between the visual objects and the defined semantic concepts is performed by taking into account both qualitative attributes of the semantic objects (e.g., color homogeneity), indicating necessary preprocessing methods (color clustering, respectively), and numerical data generated via training (e.g., color models, also defined in the ontology). To enable fast and efficient processing, this methodology is applied to MPEG-2 video, requiring only its partial decoding. The proposed approach is demonstrated in the domain of Formula-1 racing video and shows promising results.