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 | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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