Towards ontology-based cognitive vision
Machine Vision and Applications
Ontological inference for image and video analysis
Machine Vision and Applications
DiVA: A Distributed Video Analysis Framework Applied to Video-Surveillance Systems
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Representing and recognizing complex events in surveillance applications
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Semantic web technologies for video surveillance metadata
Multimedia Tools and Applications
A semantic-based probabilistic approach for real-time video event recognition
Computer Vision and Image Understanding
Semantic analysis of human movements in videos
Proceedings of the 8th International Conference on Semantic Systems
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In this paper, we propose an ontology for representing the prior knowledge related to video event analysis. It is composed of two types of knowledge related to the application domain and the analysis system. Domain knowledge involves all the high level semantic concepts in the context of each examined domain (objects, events, context...) whilst system knowledge involves the capabilities of the analysis system (algorithms, reactions to events...). The proposed ontology has been structured in two parts: the basic ontology (composed of the basic concepts and their specializations) and the domain-specific extensions. Additionally, a video analysis framework based on the proposed ontology is defined for the analysis of different application domains showing the potential use of the proposed ontology. In order to show the real applicability of the proposed ontology, it is specialized for the Underground video-surveillance domain showing some results that demonstrate the usability and effectiveness of the proposed ontology.