NDLI: Real-time foreground detection based on tempo-spatial consistency validation and Gaussian Mixture Model

Content Provider	IEEE Xplore Digital Library
Author	Qing Yan Yi Xu Xiaokang Yang Traversoni, L.
Copyright Year	2010
Description	Author affiliation: University of Autonoma Met.(Iztapalapa), Ciencias Bsicas e Ingenieria, Mexico (Traversoni, L.) \|\| Institute of Image Communication and Information Processing and Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, China (Qing Yan; Yi Xu; Xiaokang Yang)
Abstract	Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount of noises. 2) Sharp illumination changes caused by shadows, light on-off, and so on. 3) Real-time requirement for practical systems. To overcome these problems, a new approach is proposed in this paper. It is based on the background of conventional Gaussian Mixed Model, incorporating tempo-spatial consistency validation to search genuine foreground seeds, so that foreground segments can be reliably acquired using region growth method. Experiments demonstrate that our approach achieves better performance than conventional GMM approach in detection accuracy, adaptability to sudden illumination changes and computation time.
Starting Page	1
Ending Page	4
File Size	307533
Page Count	4
File Format	PDF
ISBN	9781424444618
e-ISBN	9781424444625
DOI	10.1109/ISBMSB.2010.5463088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image communication Video sequences Switches Probability Foreground Detection video processing Gaussian Mixed Model Lighting Information processing Gaussian processes tempo-spatial consistency Optical sensors Colored noise Kernel
Content Type	Text
Resource Type	Article

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