Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements.

Deep Head Pose Estimation from Depth Data for In-car Automotive Applications

BORGHI, GUIDO;CUCCHIARA, Rita
2018

Abstract

Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements.
Proceedings of the 2nd International Workshop on Understanding Human Activities through 3D Sensors
74
85
Venturelli, Marco; BORGHI, GUIDO; VEZZANI, Roberto; CUCCHIARA, Rita
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/859623
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