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.
|Data di pubblicazione:||2017|
|Titolo:||POSEidon: Face-from-Depth for Driver Pose Estimation|
|Autore/i:||Borghi, Guido; Venturelli, Marco; Vezzani, Roberto; Cucchiara, Rita|
|Nome del convegno:||2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
|Luogo del convegno:||Honolulu, Hawaii|
|Data del convegno:||July, 22-25, 2017|
|Tipologia||Relazione in Atti di Convegno|
File in questo prodotto:
I documenti presenti in Iris Unimore sono rilasciati con licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia, salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris