Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162995
Title: Toward human-centered automated driving: a novel spatiotemporal vision transformer-enabled head tracker
Authors: Hu, Zhongxu
Zhang, Yiran
Xing, Yang
Zhao, Yifan
Cao, Dongpu
Lv, Chen
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Hu, Z., Zhang, Y., Xing, Y., Zhao, Y., Cao, D. & Lv, C. (2022). Toward human-centered automated driving: a novel spatiotemporal vision transformer-enabled head tracker. IEEE Vehicular Technology Magazine, 3140047-. https://dx.doi.org/10.1109/MVT.2021.3140047
Project: W1925d0046 
M4082268.050 
Journal: IEEE Vehicular Technology Magazine 
Abstract: Accurate dynamic driver head pose tracking is of great importance for driver–automotive collaboration, intelligent copilot, head-up display (HUD), and other human-centered automated driving applications. To further advance this technology, this article proposes a low-cost and markerless headtracking system using a deep learning-based dynamic head pose estimation model. The proposed system requires only a red, green, blue (RGB) camera without other hardware or markers. To enhance the accuracy of the driver’s head pose estimation, a spatiotemporal vision transformer (ST-ViT) model, which takes an image pair as the input instead of a single frame, is proposed. Compared to a standard transformer, the ST-ViT contains a spatial–convolutional vision transformer and a temporal transformer, which can improve the model performance. To handle the error fluctuation of the head pose estimation model, this article proposes an adaptive Kalman filter (AKF). By analyzing the error distribution of the estimation model and the user experience of the head tracker, the proposed AKF includes an adaptive observation noise coefficient; this can adaptively moderate the smoothness of the curve. Comprehensive experiments show that the proposed system is feasible and effective, and it achieves a state-of-the-art performance.
URI: https://hdl.handle.net/10356/162995
ISSN: 1556-6072
DOI: 10.1109/MVT.2021.3140047
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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