Shadow Detection via Predicting the Confidence Maps of Shadow Detection Methods

Today's mainstream shadow detection methods are manually designed via a case-by-case approach. Accordingly, these methods may only be able to detect shadows for specific scenes. Given the complex and diverse shadow scenes in reality, none of the existing methods can provide a one-size-fits-all solution with satisfactory performance. To address this problem, this paper introduces a new concept, named shadow detection confidence, which can be used to evaluate the effect of any shadow detection method for any given scene. The best detection effect for a scene is achieved by combining prediction results by multiple methods. To measure the shadow detection confidence characteristics of an image, a novel relative confidence map prediction network (RCMPNet) is proposed. Experimental results show that the proposed method outperforms multiple state-of-the-art shadow detection methods on four shadow detection benchmark datasets.