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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/756
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| 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 |
| Issue Date: | 2008 |
| Publisher: | IEEE |
| Citation: | Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), December 16-19, Bhubaneswar |
| 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
res... |
| Description: | Copyright for the paper belongs to IEEE |
| URI: | http://hdl.handle.net/2080/756 |
| Appears in Collections: | Conference Papers
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Size | Format |
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| badri-pknanda-conf-2008.pdf | | 420Kb | Adobe PDF | View/Open |
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