Abstract
Human weight estimation is useful in a variety of potential applications, e.g., targeted advertisement, entertainment scenarios and forensic science. However, estimating weight only from color cues is particularly challenging since these cues are quite sensitive to lighting and imaging conditions. In this article, we propose a novel weight estimator based on a single RGB-D image, which utilizes the visual color cues and depth information. Our main contributions are three-fold. First, we construct the W8-RGBD dataset including RGB-D images of different people with ground truth weight. Second, the novel sideview shape feature and the feature fusion model are proposed to facilitate weight estimation. Additionally, we consider gender as another important factor for human weight estimation. Third, we conduct comprehensive experiments using various regression models and feature fusion models on the new weight dataset, and encouraging results are obtained based on the proposed features and models.
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This work is partially supported by Singapore Ministry of Education under Research Grant No. MOE2012-TIF-2-G-016, and also partially by the National Natural Science Foundation of China under Grant No. 61328205.
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Nguyen, T.V., Feng, J. & Yan, S. Seeing Human Weight from a Single RGB-D Image. J. Comput. Sci. Technol. 29, 777–784 (2014). https://doi.org/10.1007/s11390-014-1467-0
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DOI: https://doi.org/10.1007/s11390-014-1467-0