New Feature for Shadow Detection by Combination of Two Features Robust to Illumination Changes

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 proposed method is a shadow model based method. A new feature for detecting shadows is introduced. The feature is obtained by L*a*b* components, Peripheral Increment Sign Correlation and Normalized Vector Distance. These features are robust to illumination changes. Shadows can be treated as local illumination changes. Using these features results in removing shadow effects, in part. The histogram is generated by the three features and is treated as the feature for detecting shadows. The SVM is used for the classifier. The SVM is trained in advance by shadow data and the trained SVM is used for detecting shadows. The proposed method can extract shadows with the accuracy similar to the previous approach in shorter time. Results are demonstrated by experiments using the real videos.