Temporal Feature Warping for Video Shadow Detection

Abstract

While single image shadow detection has been improving rapidly in recent years, video shadow detection remains a challenging task due to data scarcity and the difficulty in modelling temporal consistency. The current video shadow detection method achieves this goal via co-attention, which mostly exploits information that is temporally coherent but is not robust in detecting moving shadows and small shadow regions. In this paper, we propose a simple but powerful method to better aggregate information temporally. We use an optical flow based warping module to align and then combine features between frames. We apply this warping module across multiple deep-network layers to retrieve information from neighboring frames including both local details and high-level semantic information. We train and test our framework on the ViSha dataset. Experimental results show that our model outperforms the state-of-the-art video shadow detection method by 28%, reducing BER from 16.7 to 12.0.

References in corpus (7)

• ParseNet: Looking Wider to See Better
• Shadow Detection: A Survey and Comparative Evaluation of Recent Methods
• Direction-aware Spatial Context Features for Shadow Detection
• Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
• Physics-based Shadow Image Decomposition for Shadow Removal
• Geodesic Distance Histogram Feature for Video Segmentation
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Cited by in corpus (1)

• Physics-based Shadow Image Decomposition for Shadow Removal