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Image Illumination Enhancement for Construction Worker Pose Estimation in Low-light Conditions

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13807))

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Abstract

Many construction scenes feature low-light work, such as nighttime construction and tunnel construction. Poor lighting and low visibility will increase the risk of site accidents. One of the leading causes of construction accidents is unsafe worker behavior, which can be predicted via worker posture estimation. Therefore, this study proposes an Unsupervised Illumination Reflectance Estimation (UIRE-Net) framework for estimating the dark worker pose. On the basis of lightness-color consistency, in spite of ungratified illumination conditions, the “true color” of objects depends on the illumination reflectance only. The illumination reflectance estimation is monotonous to neighboring pixel differences, making the extracted features robust for worker pose estimation. In addition, the proposed UIRE-Net restores image brightness without relying on image pairs. A testing experiment based on nighttime construction workers is conducted to validate the veracity.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Xinyu Chen & Yantao Yu

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  1. Xinyu Chen
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  2. Yantao Yu
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Correspondence to Yantao Yu .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Chen, X., Yu, Y. (2023). Image Illumination Enhancement for Construction Worker Pose Estimation in Low-light Conditions. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-25082-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25081-1

  • Online ISBN: 978-3-031-25082-8

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