Image-Based Driver Drowsiness Detection

How to extract effective features of fatigue in images and videos is important for many applications. This paper introduces a face image descriptor that can be used for discriminating driver fatigue in static frames. In this method, first, each facial image in the sequence is represented by a pyramid whose levels are divided into non-overlapping blocks of the same size, and hybrid image descriptor are employed to extract features in all blocks. Then the obtained descriptor is filtered out using feature selection. Finally, non-linear Support Vector Machines is applied to predict the drowsiness state of the subject in the image. The proposed method was tested on the public dataset NTH Drowsy Driver Detection (NTHUDDD). This dataset 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. These results show that the proposed hand-crafted feature compare favorably with several approaches based on the use of deep Convolutional Neural Nets.

[1]  Bingyou Liu,et al.  A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network , 2017 .

[2]  Mohamad Hoseyn Sigari.  Driver Hypo-vigilance Detection Based on Eyelid Behavior , 2009, 2009 Seventh International Conference on Advances in Pattern Recognition.

[3]  Esa Rahtu,et al.  Volume Local Phase Quantization for Blur-Insensitive Dynamic Texture Classification , 2011, SCIA.

[4]  Chokri Ben Amar,et al.  A Drowsy Driver Detection System Based on a New Method of Head Posture Estimation , 2014, IDEAL.

[5]  Micheal Drieberg,et al.  A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability , 2017, Sensors.

[6]  Jacob Scharcanski,et al.  Yawning Detection Using Embedded Smart Cameras , 2016, IEEE Transactions on Instrumentation and Measurement.

[7]  Aouatif Amine,et al.  Driver's fatigue detection based on yawning extraction , 2014 .

[8]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Sung Kyung Hong,et al.  Real-time categorization of driver's gaze zone using the deep learning techniques , 2016, 2016 International Conference on Big Data and Smart Computing (BigComp).

[10]  Borko Furht,et al.  Driver Drowsiness Detection , 2014, SpringerBriefs in Computer Science.

[11]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Muhammad Reza Pourshahabi,et al.  A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection , 2014 .

[13]  Borko Furht,et al.  Driver Drowsiness Detection: Systems and Solutions , 2014 .

[14]  Aurobinda Routray,et al.  A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers , 2013, IEEE Transactions on Intelligent Transportation Systems.

[15]  Tudor Mitran,et al.  Driver drowsiness detection using ANN image processing , 2017 .

[16]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[17]  Josef Kittler,et al.  Dynamic Texture Recognition Using Multiscale Binarized Statistical Image Features , 2014, IEEE Transactions on Multimedia.

[18]  Gengtian Niu,et al.  Driver Fatigue Features Extraction , 2014 .

[19]  Fei Pan,et al.  Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks , 2016, ACCV Workshops.

[20]  Anwar M. Mirza,et al.  Fully automated real time fatigue detection of drivers through Fuzzy Expert Systems , 2014, Appl. Soft Comput..

[21]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.