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dc.contributor.authorSubudhi, B N-
dc.contributor.authorNanda, P K-
dc.identifier.citationComputational Intelligence, Control, And Computer Vision In Robotics & Automation, 10-11, March 2008, NIT Rourkela, India P 108-204en
dc.descriptioncopyright for the article belongs to the Proceedings Publisheren
dc.description.abstractWe propose a novel approach of moving object detection in a video sequence. The proposed scheme uses spatial segmentation and temporal segmentation to construct the video object plane (VOP) and hence the detection of a moving objects. The spatial segmentation problem is formulated in spatiotemporal framework. A compound Markov random field model is proposed to model the video-sequences. This compound model employs edge features in the temporal direction. The MRF model parameters are selected on a trial and error basis. The labels in the spatial segmentation are estimated using Maximum a posteriori (MAP) criterion. A hybrid algorithm is proposed to obtain MAP estimates. These estimated labels are used to obtain the temporal segmentation followed by construction of video object plane. The results obtained are compared joint segmentation scheme (JSEG). It is observed that the edge based scheme proved to be best as compared to edgeless and JSEG schemes.en
dc.format.extent308570 bytes-
dc.subjectCovariance matricesen
dc.subjectFeature extractionen
dc.subjectGaussian distributionen
dc.subjectGaussian processen
dc.subjectImage edge analysisen
dc.subjectImage segmentationen
dc.subjectMAP Estimationen
dc.subjectSimulated Annealingen
dc.titleMoving Object Detection using Compound Markov Random Field Modelen
Appears in Collections:Conference Papers

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