Shadow Detection Using Tricolor Attenuation Model Enhanced with Adaptive Histogram Equalization

Shadows create significant problems in many computer vision and image analysis tasks such as object recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a shadow region as an object. So the detection of shadows in images will enhance the performance of many machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of intensity attenuation of pixels in the shadow region which is different for the three color channels. It originates from the idea that if the minimum attenuated color channel is subtracted from the maximum attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the quality of the detected shadow region.