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Journal of Engineering
Volume 2014 (2014), Article ID 615198, 9 pages
http://dx.doi.org/10.1155/2014/615198
Research Article

Motion Objects Segmentation and Shadow Suppressing without Background Learning

1School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Shanghai 200444, China
2Key Laboratory of Advanced Displays and System Application, Ministry of Education, 149 Yanchang Road, Shanghai 200072, China

Received 1 November 2013; Revised 15 December 2013; Accepted 16 December 2013; Published 23 January 2014

Academic Editor: Haranath Kar

Copyright © 2014 Y.-P. Guan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An approach to segmenting motion objects and suppressing shadows without background learning has been developed. Since wavelet transformation indicates the position of sharper variation, it is adopted to extract the information contents with the most meaningful features based on two successive video frames only. According to the fact that the saturation component is lower in the region of shadow and is independent of the brightness, HSV color space is selected to extract foreground motion region and suppress shadow instead of other color models. A local adaptive thresholding approach is proposed to extract initial binary motion masks based on the results of the wavelet transformation. A foreground reclassification is developed to get an optimal segmentation by fusion of mode filtering, connectivity analysis, and spatial-temporal correlation. Comparative studies with some investigated methods have indicated the superior performance of the proposal in extracting motion objects and suppressing shadows from cluttered contents with dynamic scene variation and crowded environments.