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Learning Shadow Correspondence for Video Shadow Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13677))

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Abstract

Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames, especially when the illumination and background textures change in a video. We make an observation that the inconsistent predictions are caused by the shadow feature inconsistency, i.e., the features of the same shadow regions show dissimilar proprieties among the nearby frames. In this paper, we present a novel Shadow-Consistent Correspondence method (SC-Cor) to enhance pixel-wise similarity of the specific shadow regions across frames for video shadow detection. Our proposed SC-Cor has three main advantages. Firstly, without requiring the dense pixel-to-pixel correspondence labels, SC-Cor can learn the pixel-wise correspondence across frames in a weakly-supervised manner. Secondly, SC-Cor considers intra-shadow separability, which is robust to the variant textures and illuminations in videos. Finally, SC-Cor is a plug-and-play module that can be easily integrated into existing shadow detectors with no extra computational cost. We further design a new evaluation metric to evaluate the temporal stability of the video shadow detection results. Experimental results show that SC-Cor outperforms the prior state-of-the-art method, by 6.51% on IoU and 3.35% on the newly introduced temporal stability metric. Code is available at https://github.com/xmed-lab/SC-Cor.

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Acknowledgement

This work was supported by a research grant from HKUST-BICI Exploratory Fund under HCIC-004 and a research grant from Foshan HKUST Projects under FSUST21-HKUST11E.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Xinpeng Ding, Jingwen Yang & Xiaomeng Li

  2. Shanghai AI Laboratory, Shanghai, China

    Xiaowei Hu

  3. The Hong Kong University of Science and Technology Shenzhen Research Institute, Kowloon, Hong Kong

    Xiaomeng Li

Authors
  1. Xinpeng Ding
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  2. Jingwen Yang
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  3. Xiaowei Hu
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  4. Xiaomeng Li
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Correspondence to Xiaomeng Li .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Ding, X., Yang, J., Hu, X., Li, X. (2022). Learning Shadow Correspondence for Video Shadow Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_42

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  • DOI: https://doi.org/10.1007/978-3-031-19790-1_42

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