Robust Shadow Detection by Exploring Effective Shadow Contexts

Effective contexts for separating shadows from non-shadow objects can appear in different scales due to different object sizes. This paper introduces a new module, Effective-Context Augmentation (ECA), to utilize these contexts for robust shadow detection with deep structures. Taking regular deep features as global references, ECA enhances the discriminative features from the parallelly computed fine-scale features and, therefore, obtains robust features embedded with effective object contexts by boosting them. We further propose a novel encoder-decoder style of shadow detection method where ECA acts as the main building block of the encoder to extract strong feature representations and the guidance to the classification process of the decoder. Moreover, the networks are optimized with only one loss, which is easy to train and does not have the instability caused by extra losses superimposed on the intermediate features among existing popular studies. Experimental results show that the proposed method can effectively eliminate fake detections. Especially, our method outperforms state-of-the-arts methods and improves over $13.97%$ and $34.67%$ on the challenging SBU and UCF datasets respectively in balance error rate.