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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/804

Title: Detection of slow moving video objects using compound Markov random Field model
Authors: Subudhi, B N
Nanda, P K
Keywords: Markov processes
image segmentation
image sequences
object detection
video signal processing
Issue Date: 2008
Publisher: IEEE
Citation: IEEE Region 10 Annual International Conference, Proceedings/TENCON,
Abstract: Often, moving object detection in a video sequence has been achieved a variant of temporal segmentation methods. For slow moving video objects, a temporal segmentation method fails to detect the objects. In this paper, we propose a Markov random Field (MRF) model based scheme to detect slow movements in a video sequence. The proposed scheme is a combination of a proposed spatio-temporal segmentation scheme and temporal segmentation method. A compound MRF model is used in spatiotemporal framework. In this framework, the a priori distribution is MRF and this takes care of spatial distribution of current frame, temporal frames and the Change Detection Masks (CDM) of the temporal frames. The spatio-temporal segmentation problem is formulated as a pixel labeling problem in Maximum a posteriori (MAP) framework. The MAP estimates are obtained using a hybrid algorithm. These estimated labels are used to obtain the Video Object Plane (VOP) and hence the detection of objects. The results are com...
Description: Copyright for the paper belongs to IEEE
URI: http://dx.doi.org/10.1109/TENCON.2008.4766385
http://hdl.handle.net/2080/804
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