A New Foreground Segmentation Method for Video Analysis in Different Color Spaces

Hang Shi, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2899-2904
Number of pages6
ISBN (Electronic)9781538637883
DOIs
StatePublished - Nov 26 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
Country/TerritoryChina
CityBeijing
Period8/20/188/24/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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