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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/653

Title: Moving Object Detection using Compound Markov Random Field Model
Authors: Subudhi, B N
Nanda, P K
Keywords: Covariance matrices
Feature extraction
Gaussian distribution
Gaussian process
Image edge analysis
Image segmentation
MAP Estimation
pattern
Simulated Annealing
Issue Date: 2008
Publisher: NITR
Citation: Computational Intelligence, Control, And Computer Vision In Robotics & Automation, 10-11, March 2008, NIT Rourkela, India P 108-204
Abstract: We 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.
Description: copyright for the article belongs to the Proceedings Publisher
URI: http://hdl.handle.net/2080/653
Appears in Collections:Conference Papers

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