NDLI: A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system

Content Provider	IEEE Xplore Digital Library
Author	Kumar, P. Sengupta, K. Lee, A.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore (Kumar, P.)
Abstract	Segmenting foreground objects of interest in real time is an important step in many applications of video surveillance, vehicle tracking and traffic monitoring. Background subtraction is a method very often used to segment moving objects in image sequences. In this paper we present a fundamental unbiased study of different color spaces for detecting foreground objects and their shadows in image sequences. Different color spaces show different efficiency in detection of foreground objects and their shadows. This empirical study was done with the motivation of determining, which color space is best for foreground segmentation and shadow detection. This study of the quality of foreground and shadow detection as a function of color space is unique kind and especially relevant to color image sequences. Our study includes five color spaces "RGB", "XYZ". "YC/sub r/C/sub b/", "HSV" and the normalized "rgb". We use an empirically substantiated model of shadows formulate the detection scheme for each color space. We use a statistical technique to model the background pixels. The results are compared in terms of true detection, misses and false detection of pixels and also detection of the moving foreground objects as blobs. The results show that "YC/sub r/C/sub b/" is the best color space for optimal foreground and shadow detection.
Starting Page	100
Ending Page	105
File Size	453271
Page Count	6
File Format	PDF
ISBN	0780373898
DOI	10.1109/ITSC.2002.1041196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computerized monitoring Object detection Motion detection Image segmentation Drives Image sequences Image color analysis Vehicle detection Computer vision Intelligent vehicles
Content Type	Text
Resource Type	Article

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