Simple Hybrid Camera-Based System Using Two Views for Three-Dimensional Body Measurements

Abstract

Using a single RGB camera to obtain accurate body dimensions, rather than measuring these manually or via more complex multicamera systems or more expensive 3D scanners, has a high application potential for the apparel industry. We present a system that estimates upper human body measurements using a hybrid set of techniques from both classic computer vision and recent machine learning. The main steps involve (1) using a camera to obtain two views (frontal and side); (2) isolating in the image pair a set of main body parts; (3) improving the image quality; (4) extracting body contours and features from the images of body parts; (5) indicating markers on these images; (6) performing a calibration step; and (7) producing refined final 3D measurements. We favour a unique geometric shape, that of an ellipse, to approximate human body main horizontal cross-sections. We focus on the more challenging parts of the body, i.e., the upper body from the head to the hips, which, we show, can be well represented by varying an ellipse’s eccentricity for each individual. Then, evaluating each fitted ellipse’s perimeter allows us to obtain better results than the current state-of-the-art methods for use in the fashion and online retail industry. In our study, we selected a set of two equations, out of many other possible choices, to best estimate upper human body section circumferences. We experimented with the system on a diverse sample of 78 female participants. The results for the upper human body measurements in comparison to the traditional manual method of tape measurements, when used as a reference, show ±1 cm average differences, which are sufficient for many applications, including online retail.