Paper Info
Title
In-attention State Monitoring for a Driver Based on Head Pose and Eye Blinking Detection Using One Class Support Vector Machine.
Abstract
This paper proposes a model to detect inattention cognitive state of a driver during various driving situations. The proposed system predicts driver's inattention state based on the analysis of eye blinking patterns and head pose direction. The study uses an infrared camera and several feature extraction stages such as modified census transform (MCT) to reduce the effect of light source change in real traffic environment. Also, we propose a new eye blinking detection using the difference between center and surround of Hough circle transform image. The local linear embedding (LLE) is used to extract real-time features of head movement. Finally, the driver's cognitive states can be estimated by the one-class support vector machines (OCSVMs) using both eyes blinking patterns and head pose direction information. We implement a prototype of the proposed driver state monitoring (DSM) system. Experimental results show that the proposed system using OCSVM works well in real environment compared to the system that employs SVM.
Year
DOI
Venue
2014
10.1007/978-3-319-12640-1_14
Lecture Notes in Computer Science
Keywords
Field
DocType
One class SVM,Inattention detection,Eye blink,Driver state monitoring
Computer vision,Embedding,Pattern recognition,Computer science,Support vector machine,Feature extraction,Census transform,Artificial intelligence,Eye blinking,Eye blink,Light source
Conference
Volume
ISSN
Citations 
8835
0302-9743
5
PageRank 
References 
Authors
0.51
7
2
Name
Order
Citations
PageRank
Hyunrae Jo150.51
Minho Lee2946.96