Depth Based Context Modeling and Classification in Video-surveillance
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | Proceedings of the International Conference on Machine Vision and Machine Learning |
| Year: | 2014 |
| Month: | August |
| 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. |
| Keywords: | Context modelling, Pattern Recognition, Scene segmentation, Video-surveillance |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
|
Topics
|
|
|
|
