Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models. (September 2017)

• Record Type: Journal Article
• Authors: Drory, Ami; Li, Hongdong; Hartley, Richard
• Other Names: Goff John Eric guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 3(2017:Sep.)
• Page Start: 169
• Page End: 183
• Publication Date: 2017-09
• Subjects: Statistical shape model -- appearance model -- gradient vector flow -- cycling aerodynamics -- pose estimation -- variational methods -- segmentation; Sports sciences -- Periodicals; Sporting goods -- Design and construction -- Periodicals; Sports -- Technological innovations -- Periodicals; Biomechanics -- Periodicals; 613.71
• DOI: 10.1177/1754337117705489
• Languages: English
• ISSNs: 1754-3371

Abstract

We present a computer vision-based approach to estimating the projected frontal surface area (pFSA) of cyclists from unconstrained images. Wind tunnel studies show a reduction in cyclists' aerodynamic drag through manipulation of the cyclist's pose. Whilst the mechanism by which reduction is achieved remains unknown, it is widely accepted in the literature that the drag is proportional to the cyclist's pFSA. This paper describes a repeatable automatic method for pFSA estimation for the study of its relationship with aerodynamic drag in cyclists. The proposed approach is based on finding object boundaries in images. An initialised curve dynamically evolves in the image to minimise an energy function designed to force the curve to gravitate towards image features. To overcome occlusions and pose variation, we use a statistical cyclist shape and appearance models as priors to encourage the evolving curve to arrive at the desired solution. Contour initialisation is achieved using a discriminative object detection method based on offline supervised learning that yields a cyclist classifier. Once an instance of a cyclist is detected in an image and segmented, the pFSA is calculated from the area of the final curve. Applied to two challenging datasets of cyclist images, for cyclist detection our method achieves precision scores of 1.0 and 0.96 and recall scores of 0.68 and 0.83 on the wind tunnel and cyclists-in-natura datasets, respectively. For cyclist segmentation, it achieves 0.88 and 0.92 scores for the mean dice similarity coefficient metric on the two datasets, respectively. We discuss the performance of our method under occlusion, orientation, and pose conditions. Our method successfully estimates pFSA of cyclists and opens new vistas for exploration of the relationship between pFSA and aerodynamic drag.