Titlebook: Computer Vision – ACCV 2016 Workshops; ACCV 2016 Internatio Chu-Song Chen,Jiwen Lu,Kai-Kuang Ma Conference proceedings 2017 Springer Intern

枫树

Axon895

Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networ in hopes of preventing an accident. This paper proposes a deep architecture referred to as deep drowsiness detection (DDD) network for learning effective features and detecting drowsiness given a RGB input video of a driver. The DDD network consists of three deep networks for attaining global robu

NOMAD

聚集

辫子带来帮助

3D Pose Estimation of a Front-Pointing Hand Using a Random Regression Forestom a distance. Our method uses a Random Regression Forest (RRF) to realize robust estimation against environmental and individual variations. In order to improve the estimation accuracy, our method supports the use of two cameras and integrates the distributions of the hand poses for these cameras,

调色板

adumbrate

flammable

制造

乳白光

Development of the Female Perineum,of the three networks are integrated and fed to a softmax classifier for drowsiness detection. Experimental results show that DDD achieves . detection accuracy on NTHU-drowsy driver detection benchmark dataset.