Shadow Detection by Three Shadow Models with Features Robust to Illumination Changes

Abstract Computer vision methods need to deal with shadows explicitly because shadows often have a negative effect on the results computed. A new shadow detection method is proposed. The new method constructs three shadow models. Three features robust to illumination changes are used to construct the models. The method uses color information, Peripheral Increment Sign Correlation image and edge information. Each of these features removes shadow effects, in part. The overall method can construct an effective shadow model by using all of the features. The result is improved further by region based analysis and by online update of the shadow model. The proposed method extracts shadows accurately. Results are demonstrated by experiments using the real videos of outdoor scenes.

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