NDLI: Shadow elimination for effective moving object detection with Gaussian models

Content Provider	IEEE Xplore Digital Library
Author	Chia-Jung Chang Wen-Fong Hu Jun-Wei Hsieh Yung-Sheng Chen
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. Eng., Yuan Ze Univ., Taiwan (Chia-Jung Chang; Wen-Fong Hu; Jun-Wei Hsieh; Yung-Sheng Chen)
Abstract	This paper presents a coarse-to-fine approach to eliminate unexpected shadows of multiple pedestrians from a static and textured background using Gaussian shadow modeling. At the coarse stage, a moment-based method is proposed to estimate the rough boundaries between shadows and moving objects. Then, at the fine stage, the rough approximation of shadow region provides a key to model shadows. The chosen shadow model is parameterized with several features including the orientation, mean, and center position of a shadow region. With these features, the chosen model can precisely eliminate the unexpected shadows from the scene background and thus improve the quality of further content analysis. Experiments demonstrate approximately 95% ratio of pedestrian-related shadows can be successfully eliminated from the scene background.
Starting Page	540
Ending Page	543
File Size	276803
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Cameras Lighting Video sequences Layout Argon Video surveillance Teleconferencing Real time systems Failure analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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