Driver Fatigue Features Extraction | Zendy

Gengtian Niu | Zendy; Wang Chang-ming | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Driver Fatigue Features Extraction

Author(s) -

Gengtian Niu,

Wang Chang-ming

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/860517

Subject(s) - artificial intelligence , block (permutation group theory) , computer science , adaboost , pattern recognition (psychology) , process (computing) , image (mathematics) , feature extraction , sequence (biology) , computer vision , mathematics , support vector machine , geometry , biology , genetics , operating system

Driver fatigue is the main cause of traffic accidents. How to extract the effective features of fatigue is important for recognition accuracy and traffic safety. To solve the problem, this paper proposes a new method of driver fatigue features extraction based on the facial image sequence. In this method, first, each facial image in the sequence is divided into nonoverlapping blocks of the same size, and Gabor wavelets are employed to extract multiscale and multiorientation features. Then the mean value and standard deviation of each block’s features are calculated, respectively. Considering the facial performance of human fatigue is a dynamic process that developed over time, each block’s features are analyzed in the sequence. Finally, Adaboost algorithm is applied to select the most discriminating fatigue features. The proposed method was tested on a self-built database which includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research