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Real-time head orientation from a monocular camera using deep neural network

Cited 43 time in Web of Science Cited 29 time in Scopus
Authors

Ahn, Byungtae; Park, Jaesik; Kweon, In So

Issue Date
2015
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science, Vol.9005, pp.82-96
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 32 × 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 1ms 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.
ISSN
0302-9743
URI
https://hdl.handle.net/10371/201328
DOI
https://doi.org/10.1007/978-3-319-16811-1_6
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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