Study on Color Space Selection for Detecting Cast Shadows in Video Surveillance

摘要

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.