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Depth Based Context Modeling and Classification in Video-surveillance
Art der Publikation: Artikel in einem Konferenzbericht
Zitat:
Buchtitel: Proceedings of the International Conference on Machine Vision and Machine Learning
Jahr: 2014
Monat: August
Ort: Prague, Czech Republic
Organisation: International Conference on Machine Vision and Machine Learning, 14-15.08.2014
Abriss: With a dedicated definition of ‘Context’ in image understanding systems, we present in this paper a novel context modelling and classification system. The main goal behind multimodal context modelling is to identify the context type from video-surveillance footage of multipurpose halls. First, the distribution of the different zones in a multipurpose hall is automatically captured using a dedicated depth based segmentation method. The discriminative description is illustrated by extracting five semantic features according to depth zones. These features are processed with the Transferable Belief Model to propose a classification. Results show the validity of the method for context recognition.
Schlagworte: Context modelling, Pattern Recognition, Scene segmentation, Video-surveillance
Autoren Charara, Nour
Abou Khaled, Omar
Mugellini, Elena
Jarkass, Iman
Maria, Sokhn
Hinzugefügt von: []
Gesamtbewertung: 0
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  • MVML2014_DBCoM.pdf
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