NDLI: Moving cast shadow detection from a Gaussian mixture shadow model

Content Provider	IEEE Xplore Digital Library
Author	Martel-Brisson, N. Zaccarin, A.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Laval Univ., Que., Canada (Martel-Brisson, N.; Zaccarin, A.)
Abstract	Moving cast shadows are a major concern for foreground detection algorithms. Processing of foreground images in surveillance applications typically requires that such shadows have been identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of non-uniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, and prevent false detection in regions where shadows cannot be detected. Gaussian mixture shadow models (GMSM) are automatically constructed and updated over time, are easily added to GMM architecture for foreground detection, and require only a small number of parameters. Results obtained with different scene types show the robustness of the approach.
Sponsorship	IEEE Comput. Soc
Starting Page	643
Ending Page	648
File Size	522794
Page Count	6
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.233
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Lighting Brightness Detection algorithms Surveillance Robustness Image segmentation Solid modeling Geometry Computer vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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