TY  - CONF
T1  - Depth Based Context Modeling and Classification in Video-surveillance
A1  - Charara, Nour
A1  - Abou Khaled, Omar
A1  - Mugellini, Elena
A1  - Jarkass, Iman
A1  - Maria, Sokhn
TI  - Proceedings of the International Conference on Machine Vision and Machine Learning
Y1  - 2014
T2  - International Conference on Machine Vision and Machine Learning, 14-15.08.2014
CY  - Prague, Czech Republic
KW  - Context modelling
KW  - Pattern Recognition
KW  - Scene segmentation
KW  - Video-surveillance
N2  - 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.
ER  -