Document Type

Article

Publication Date

9-2014

Publication Source

Journal of Computer Science and Technology

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.

Inclusive pages

777–784

ISBN/ISSN

1000-9000

Document Version

Postprint

Comments

The document is available for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Differences may exist between this document and the published version, which is available using the link provided. Permission documentation is on file.

Publisher

Springer

Volume

29

Issue

5

Peer Reviewed

yes