%Aigaion2 BibTeX export van HES SO Valais Publications
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@INPROCEEDINGS{,
        author = {Charara, Nour and Abou Khaled, Omar and Mugellini, Elena and Jarkass, Iman and Maria, Sokhn},
      keywords = {Context modelling, Pattern Recognition, Scene segmentation, Video-surveillance},
         month = aug,
         title = {Depth Based Context Modeling and Classification in Video-surveillance},
     booktitle = {Proceedings of the International Conference on Machine Vision and Machine Learning},
          year = {2014},
      location = {Prague, Czech Republic},
  organization = {International Conference on Machine Vision and Machine Learning, 14-15.08.2014},
      abstract = {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.}
}