Abstract
To automatically estimate the anthropometric measurements of individuals is an important step for ergonomic researches and customized product design. With the development of 3D scanning technology, several automatic or semi-automatic estimation systems based on the scanned 3D models are proposed to substitute for the time-consuming and inaccurate manual estimation. This 3D scanning system can also be utilized to rapidly collect large amount of human geometric information for further analysis. However, the instrumental cost and the required installation space of 3D laser scanner limit the practical applications. In this thesis, an automatic anthropometric measurement estimation system based on a low-cost RGB-D camera and a pre-learned statistical model is proposed. The proposed system includes an off-line parametric model learning stage and an on-line measurement estimation stage. A series of image processing, computer vision, and statistical machine learning algorithms, such as principal component analysis (PCA), linear regression, and artificial neural networks (ANN), are applied to analyze 3D scanned bodies in the off-line stage. In the on-line testing stage, the depth map captured by the RGB-D camera is used to synthesize the whole-body 3D model with 3D point cloud registration algorithm. The estimated surface deformation parameters can be further applied to pre-learned parametric models for anthropometric measurement estimation. The experimental results show that the proposed system based on a commercial RGB-D camera can effectively estimate nine anthropometric measurements, including six length measurements and three girth dimensions. The results of simulation experiments and real depth data experiments also demonstrate the robustness of the proposed system with three different types of depth inputs. Finally, the cluster analysis can improve the estimation accuracy with gender classifiers and dynamic parametric models. Compared with the state-of-the-art systems based on RGB-D cameras, the proposed system can estimate anthropometric measurements accurately and efficiently.