  Shadow detection in a single image has received significant research interest
in recent years. However, much fewer works have been explored in shadow
detection over dynamic scenes. The bottleneck is the lack of a well-established
dataset with high-quality annotations for video shadow detection. In this work,
we collect a new video shadow detection dataset, which contains 120 videos with
11, 685 frames, covering 60 object categories, varying lengths, and different
motion/lighting conditions. All the frames are annotated with a high-quality
pixel-level shadow mask. To the best of our knowledge, this is the first
learning-oriented dataset for video shadow detection. Furthermore, we develop a
new baseline model, named triple-cooperative video shadow detection network
(TVSD-Net). It utilizes triple parallel networks in a cooperative manner to
learn discriminative representations at intra-video and inter-video levels.
Within the network, a dual gated co-attention module is proposed to constrain
features from neighboring frames in the same video, while an auxiliary
similarity loss is introduced to mine semantic information between different
videos. Finally, we conduct a comprehensive study on ViSha, evaluating 12
state-of-the-art models (including single image shadow detectors, video object
segmentation, and saliency detection methods). Experiments demonstrate that our
model outperforms SOTA competitors.
