Title: Model-based characterisation of vehicle occupants using a depth camera

Authors: Byoung-Keon D. Park; Jian Wan; Ksenia Kozak; Matthew P. Reed

Addresses: Transportation Research Institute, University of Michigan, 48109, USA ' Research and Advanced Engineering, Ford Motor Company, 48121, USA ' Research and Advanced Engineering, Ford Motor Company, 48121, USA ' Transportation Research Institute, University of Michigan, 48109, USA

Abstract: Due to recent advances in sensing technologies, modern vehicle occupant classification systems enable personalised vehicle experiences and adaptive occupant crash protection. However, most systems are limited to occupant detection and simple classification, and thus, accurate estimation of body characteristics is needed to support more advanced occupant classification. This paper presents a model-based characterisation method for vehicle occupants using a 3D depth camera. This method automatically estimates standard anthropometric data of an occupant such as stature and weight along with the body shape by fitting a statistical body shape model to depth image data. The system is even robust to a wide range of clothing and is capable of generating accurate results. A variety of other algorithms were developed to improve the fitting result, including seat geometry detection and head location estimation. The new capability has a range of potential applications for improving occupant safety and providing an optimised interior configuration for the occupant.

Keywords: occupant characterisation; occupant detection; occupant classification; time-of-flight camera; depth camera; statistical body shape model; body shape estimation; anthropometry; model fitting.

DOI: 10.1504/IJVD.2020.114780

International Journal of Vehicle Design, 2020 Vol.83 No.1, pp.23 - 37

Received: 22 Oct 2019
Accepted: 18 May 2020
Published online: 06 May 2021 *

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