Temporal Feature Warping for Video Shadow Detection

• URL: http://arxiv.org/abs/2107.14287v1
• Date: Thu, 29 Jul 2021 19:12:50 GMT
• Title: Temporal Feature Warping for Video Shadow Detection
• Authors: Shilin Hu, Hieu Le, Dimitris Samaras
• Abstract summary: 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.
• Score: 30.82493923485278
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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