Real-Time Head Pose Estimation Using Weighted Random Forests

2014 ◽  
pp. 554-562 ◽  
Author(s):  
Hyunduk Kim ◽  
Myoung-Kyu Sohn ◽  
Dong-Ju Kim ◽  
Nuri Ryu
Keyword(s):  
Real Time ◽  
Random Forests ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose

scholarly journals Near Real-Time Three Axis Head Pose Estimation Without Training

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 64256-64265 ◽  
Author(s):  
Andrea F. Abate ◽  
Paola Barra ◽  
Carmen Bisogni ◽  
Michele Nappi ◽  
Stefano Ricciardi
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose

Robust real-time 3D head pose estimation from range data

2005 ◽  
Vol 38 (8) ◽  
pp. 1153-1165 ◽  
Author(s):  
Sotiris Malassiotis ◽  
Michael G. Strintzis
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Range Data ◽  
Head Pose ◽  
3D Head Pose Estimation

Enhanced real-time head pose estimation system for mobile device

2014 ◽  
Vol 21 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Euclides N. Arcoverde Neto ◽  
Rafael M. Duarte ◽  
Rafael M. Barreto ◽  
João Paulo Magalhães ◽  
Carlos C.M. Bastos ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Mobile Device ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Estimation System

scholarly journals Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

2021 ◽  
Vol 8 (2) ◽  
pp. 3-7
Author(s):  
Julkar Nine ◽  
Naeem Ahmed ◽  
Rahul Mathavan
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Situation Awareness ◽  
Automobile Industry ◽  
Identification Accuracy ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Embedded Devices ◽  
Falling Asleep ◽  
Technical Solutions

the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones

Real-Time Head Pose Estimation and Face Modeling From a Depth Image

2019 ◽  
Vol 21 (10) ◽  
pp. 2473-2481 ◽  
Author(s):  
Changwei Luo ◽  
Juyong Zhang ◽  
Jun Yu ◽  
Chang Wen Chen ◽  
Shengjin Wang
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Depth Image ◽  
Head Pose ◽  
Face Modeling
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