Motion Objects Segmentation and Shadow Suppressing without Background Learning
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Guan, Y.-P.. 2014. Motion Objects Segmentation and Shadow Suppressing without Background Learning. Journal of Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1040429
Modern Language Association (MLA)
Guan, Y.-P.. Motion Objects Segmentation and Shadow Suppressing without Background Learning. Journal of Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1040429
American Medical Association (AMA)
Guan, Y.-P.. Motion Objects Segmentation and Shadow Suppressing without Background Learning. Journal of Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1040429
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1040429