It is well-recognized that correctly detecting shadow boundaries is much more difficult than shadow interior pixels in real-world situations due to the rapid intensity and color transitions near shadow boundaries and the complex background textures. This paper presents a novel boundary-aware shadow detection network to enhance the overall detection performance, especially on challenging shadow boundaries. To enable the network aware of shadow boundaries and balance the supervision between image pixels on the boundary and inside of shadows, the shadow mask is decoupled into a body mask and boundary mask. The feature correction module is designed after feature interaction to eliminate various shadow inferences further. Experimental results show that the proposed network outperforms the state-of-the-art shadow detection methods on four shadow detection benchmark datasets.

Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this article proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints. The framework splits each frame into a tracked region, which is the region tracked from the previous video frame through optical flow analysis, and an emerging region, which is newly introduced into the scene due to the moving viewpoint. The framework subsequently extracts features based on the intensity profiles surrounding the boundaries of candidate shadow regions. These features are then utilized to both correct erroneous shadow boundaries for the tracked region and to detect shadow boundaries for the emerging region by a Bayesian learning module. To remove spurious shadows, spatial layout constraints are further considered for emerging regions. The experimental results demonstrate that the proposed framework outperforms the state-of-the-art shadow tracking and detection algorithms on a variety of challenging cases in real time, including shadows on backgrounds with complex textures, nonplanar shadows, fast-moving shadows with changing typologies, and shadows cast by nonrigid objects. The quantitative experiments show that our method outperforms the best existing method, achieving a 33.3% increase in the average$F_{measure}$on a self-collected database. Coupled with an image-based shadow-casting method, the proposed framework generates realistic shadow interaction results. This capability will be particularly beneficial for supporting AR applications.

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.