NDLI: An integrated background model for video surveillance based on primal sketch and 3D scene geometry

Content Provider	IEEE Xplore Digital Library
Author	Wenze Hu Haifeng Gong Song-Chun Zhu Yontian Wang
Copyright Year	2008
Description	Author affiliation: Lotus Hill Inst., Ezhou (Wenze Hu; Haifeng Gong; Song-Chun Zhu)
Abstract	This paper presents a novel integrated background model for video surveillance. Our model uses a primal sketch representation for image appearance and 3D scene geometry to capture the ground plane and major surfaces in the scene. The primal sketch model divides the background image into three types of regions - flat, sketchable and textured. The three types of regions are modeled respectively by mixture of Gaussians, image primitives and LBP histograms. We calibrate the camera and recover important planes such as ground, horizontal surfaces, walls, stairs in the 3D scene, and use geometric information to predict the sizes and locations of foreground blobs to further reduce false alarms. Compared with the state-of-the-art background modeling methods, our approach is more effective, especially for indoor scenes where shadows, highlights and reflections of moving objects and camera exposure adjusting usually cause problems. Experiment results demonstrate that our approach improves the performance of background/foreground separation at pixel level, and the integrated video surveillance system at the object and trajectory level.
Starting Page	1
Ending Page	8
File Size	1116993
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587541
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solid modeling Video surveillance Layout Geometry Cameras Context modeling Gaussian distribution Gaussian processes Histograms Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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