Two-Stage Video Shadow Detection via Temporal-Spatial Adaption

Abstract

Video Shadow Detection (VSD) is an important computer vision task focusing on detecting and segmenting shadows throughout the entire video sequence. Despite their remarkable performance, existing VSD methods and datasets mainly focus on the dominant and isolated shadows. Consequently, VSD under complex scenes is still an unexplored challenge. To address this issue, we built a new dataset, Complex Video Shadow Dataset (CVSD), which contains 196 video clips including 19,757 frames with complex shadow patterns, to enhance the practical applicability of VSD. We propose a two-stage training paradigm and a novel network to handle complex dynamic shadow scenarios. Regarding the complex video shadow detection as conditioned feature adaption, we propose temporal- and spatial-adaption blocks for incorporating temporal information and attaining high-quality shadow detection, respectively. To the best of our knowledge, we are the first to construct the dataset and model tailored for the complex VSD task. Experimental results show the superiority of our model over state-of-the-art VSD methods. Our project will be publicly available at: https://hizuka590.github.io/CVSD.