Cast shadow cause serious problem in the extracting of moving objects because shadow pixels are liable to be
misclassified as foreground. Many methods of cast shadow removal have been proposed and many features are selected
in these methods. But since, moving object (MO) and cast shadow are classified by a single linear classifier. As it is
known, each feature has its strength and weakness and is particularly applicable for handling a certain type of variation.
In this paper, a novel framework for feature selection for cast shadow removal based on AdaBoost is proposed.
Experiments are conducted on many scenes and the results prove the validation of the proposed method.