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Study on color space selection for detecting cast shadows in video surveillance
Author(s) -
Benedek Csaba,
Szirányi Tamás
Publication year - 2007
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20110
Subject(s) - chrominance , artificial intelligence , color space , computer vision , computer science , rgb color model , shadow (psychology) , icc profile , color balance , color depth , luminance , color histogram , segmentation , grayscale , color model , rgb color space , pixel , color image , image (mathematics) , image processing , psychology , psychotherapist
In this article, the authors address the color modeling problem of cast shadows in video sequences. It is illustrated that the performance of shadow detection can be improved significantly through appropriate color space selection, if for practical purposes, the number of free parameters of the method should be kept low. Hence, the authors compare several well known color spaces with a six‐parameter shadow model embedded into a globally optimal MRF framework. Experimental results are shown regarding the following questions: (1) What is the gain of using color images instead of grayscale ones? (2) What is the gain of using uncorrelated spaces instead of the standard RGB? (3) Chrominance (illumination invariant), luminance, or mixed spaces are more effective? (4) In which scenes are the differences significant? The authors qualified the metrics both in color based clustering of the individual pixels and in the case of Bayesian foreground‐background‐shadow segmentation. Experimental results on real‐life videos show that CIE L*u*v* color space is the most efficient. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 190–201, 2007