In-Vehicle Occupant Head Tracking Using aLow-Cost Depth Camera

2018-01-1172

04/03/2018

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Event
WCX World Congress Experience
Authors Abstract
Content
Analyzing dynamic postures of vehicle occupants in various situations is valuable for improving occupant accommodation and safety. Accurate tracking of an occupant’s head is of particular importance because the head has a large range of motion, controls gaze, and may require special protection in dynamic events including crashes. Previous vehicle occupant posture studies have primarily used marker-based optical motion capture systems or multiple video cameras for tracking facial features or markers on the head. However, the former approach has limitations for collecting on-road data, and the latter is limited by requiring intensive manual postprocessing to obtain suitable accuracy. This paper presents an automated on-road head tracking method using a single Microsoft Kinect V2 sensor, which uses a time-of-flight measurement principle to obtain a 3D point cloud representing objects in the scene at approximately 30 Hz. Vehicle passenger motions were recorded during hard braking and rapid lane changes. The dynamic head orientation and location data were obtained by fitting a subject-specific 3d head model to the depth data from each frame. Results were validated using a marker-based tracking system based on video images from multiple views. The results showed that the proposed method and system provides efficient and accurate in-vehicle head tracking using a single low-cost depth camera. Extensions of this method have broad applications for study of vehicle occupant dynamics, and with advances in technology may be applicable to routine use in production vehicles.
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DOI
https://doi.org/10.4271/2018-01-1172
Pages
7
Citation
Park, B., Jones, M., Miller, C., Hallman, J. et al., "In-Vehicle Occupant Head Tracking Using aLow-Cost Depth Camera," SAE Technical Paper 2018-01-1172, 2018, https://doi.org/10.4271/2018-01-1172.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-1172
Content Type
Technical Paper
Language
English