Seeing Human Weight from a Single RGB-D Image |
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Authors: | Tam V. Nguyen Jiashi Feng Shuicheng Yan |
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Affiliation: | 1. ARTIC Centre, Department for Technology, Innovation and Enterprise, Singapore Polytechnic, Singapore, 139651, Singapore 2. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94720, U.S.A. 3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
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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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Keywords: | RGB-D image depth information human weight estimation |
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