Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth approach for learning image faces from depth. Results in face reconstruction are qualitatively impressive. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.

POSEidon: Face-from-Depth for Driver Pose Estimation / Borghi, Guido; Venturelli, Marco; Vezzani, Roberto; Cucchiara, Rita. - 2017-:(2017), pp. 5494-5503. ((Intervento presentato al convegno 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 tenutosi a Honolulu, Hawaii nel July, 22-25, 2017 [10.1109/CVPR.2017.583].

POSEidon: Face-from-Depth for Driver Pose Estimation

BORGHI, GUIDO;VEZZANI, Roberto;CUCCHIARA, Rita
2017-01-01

Abstract

Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth approach for learning image faces from depth. Results in face reconstruction are qualitatively impressive. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.
30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Honolulu, Hawaii
July, 22-25, 2017
2017-
5494
5503
Borghi, Guido; Venturelli, Marco; Vezzani, Roberto; Cucchiara, Rita
POSEidon: Face-from-Depth for Driver Pose Estimation / Borghi, Guido; Venturelli, Marco; Vezzani, Roberto; Cucchiara, Rita. - 2017-:(2017), pp. 5494-5503. ((Intervento presentato al convegno 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 tenutosi a Honolulu, Hawaii nel July, 22-25, 2017 [10.1109/CVPR.2017.583].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1127609
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