Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network

We propose an efficient and accurate head orientation estimation algorithm using a monocular camera. Our approach is leveraged by deep neural network and we exploit the architecture in a data regression manner to learn the mapping function between visual appearance and three dimensional head orienta...

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Bibliographic Details
Published inComputer Vision -- ACCV 2014 Vol. 9005; pp. 82 - 96
Main Authors Ahn, Byungtae, Park, Jaesik, Kweon, In So
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Convolutional Neural Network
Graphic Processing Unit
Image processing
Input Image
Particle Filter
Pattern recognition
Random Forest
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Summary:We propose an efficient and accurate head orientation estimation algorithm using a monocular camera. Our approach is leveraged by deep neural network and we exploit the architecture in a data regression manner to learn the mapping function between visual appearance and three dimensional head orientation angles. Therefore, in contrast to classification based approaches, our system outputs continuous head orientation. The algorithm uses convolutional filters trained with a large number of augmented head appearances, thus it is user independent and covers large pose variations. Our key observation is that an input image having $$32 \times 32$$ resolution is enough to achieve about 3 degrees of mean square error, which can be used for efficient head orientation applications. Therefore, our architecture takes only 1 ms on roughly localized head positions with the aid of GPU. We also propose particle filter based post-processing to enhance stability of the estimation further in video sequences. We compare the performance with the state-of-the-art algorithm which utilizes depth sensor and we validate our head orientation estimator on Internet photos and video.
Bibliography:Original Abstract: We propose an efficient and accurate head orientation estimation algorithm using a monocular camera. Our approach is leveraged by deep neural network and we exploit the architecture in a data regression manner to learn the mapping function between visual appearance and three dimensional head orientation angles. Therefore, in contrast to classification based approaches, our system outputs continuous head orientation. The algorithm uses convolutional filters trained with a large number of augmented head appearances, thus it is user independent and covers large pose variations. Our key observation is that an input image having \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$32 \times 32$$\end{document} resolution is enough to achieve about 3 degrees of mean square error, which can be used for efficient head orientation applications. Therefore, our architecture takes only 1 ms on roughly localized head positions with the aid of GPU. We also propose particle filter based post-processing to enhance stability of the estimation further in video sequences. We compare the performance with the state-of-the-art algorithm which utilizes depth sensor and we validate our head orientation estimator on Internet photos and video.
ISBN:9783319168104
331916810X
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-16811-1_6