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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/875

Title: An Evolutionary Based Slow and Fast Moving Video Object Detection Scheme Using Compound Markov Random Field Model
Authors: Subudhi, B N
Nanda, P K
Keywords: Markov processes
evolutionary computation
image motion analysis
image resolution
image segmentation
image sequences
Issue Date: 2008
Publisher: IEEE
Citation: Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, December 16-19, 2008. ICVGIP
Abstract: We propose an evolving scheme to detect slow as well as fast moving objects in a video sequence. The proposed scheme employ both spatio-temporal and temporal segmentation to obtain the video object plane and hence detection. We propose a compound Markov random field model as the a priori image model that takes into account the spatial distribution of the current frame, temporal frames and the edge maps of the temporal frames. The spatio-temporal segmentation is cast as a pixel labeling problem and the labels are the MAP estimates. The MAP estimates of a frame are obtained by a hybrid algorithm. The spatial segmentation of a given frame evolves to generate the spatial segmentation of the subsequent frames. The evolved spatial segmentation together with the temporal segmentation produces the Video Object Plane (VOP) and hence detection. Our scheme does require the computation of spatio-temporal segmentation of the initial frame thus speeding up the whole process. The results of the propo...
URI: http://10.1109/ICVGIP.2008.38
http://hdl.handle.net/2080/875
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