A New Foreground Segmentation Method for Video Analysis in Different Color Spaces
Document Type
Conference Proceeding
Publication Date
11-26-2018
Abstract
A new foreground segmentation method is presented in this paper for video analysis. Specifically, a new feature representation scheme is first proposed in different color spaces, namely, the RGB, the YIQ, and the YCbCr color spaces. The new feature vector, which integrates the color values in a particular color space, the horizontal and vertical Haar wavelet features, and the temporal difference features, enhances the discriminatory power. A new Global Foreground Modeling (GFM) method is then presented to improve upon the popular video analysis approaches. The Bayes classifier is finally applied for foreground segmentation in video. Experimental results using the New Jersey Department of Transportation (NJDOT) traffic video sequences show that the new foreground segmentation method achieves better performance than the popular video analysis methods.
Identifier
85059740599 (Scopus)
ISBN
[9781538637883]
Publication Title
Proceedings International Conference on Pattern Recognition
External Full Text Location
https://doi.org/10.1109/ICPR.2018.8545500
ISSN
10514651
First Page
2899
Last Page
2904
Volume
2018-August
Grant
1647170
Fund Ref
Norsk Sykepleierforbund
Recommended Citation
Shi, Hang and Liu, Chengjun, "A New Foreground Segmentation Method for Video Analysis in Different Color Spaces" (2018). Faculty Publications. 8246.
https://digitalcommons.njit.edu/fac_pubs/8246
