Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State

ABSTRACT:

Driver’s fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in working conditions. In this paper, we propose a vision-based fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure (PERCLOS) estimation, and fatigue level classification. The core innovative techniques are as follows: 1) an approach to estimate the continuous level of eye openness based on spectral regression; and 2) a fusion algorithm to estimate the eye state based on adaptive integration on the multimodel detections of both eyes. A robust measure of PERCLOS on the continuous level of eye openness is defined, and the driver states are classified on it. In experiments, systematic evaluations and analysis of proposed algorithms, as well as comparison with ground truth on PERCLOS measurements, are performed. The experimental results show the advantages of the system on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver’s face is used for driving state monitoring.

EXISTING SYSTEM:

• A drowsy driver displays a number of symptoms, including frequent eye-closure, rapid and constant blinking, nodding or swinging head, and frequent yawning. In the last decade, numerous vision systems have been developed to detect such behaviors of drowsiness for driving safety.

• Most of the existing systems require the installation of a camera directly toward the driver’s face to capture high-resolution face images, and some of them employ specifically designed infra-red (IR) cameras, or stereo cameras.

• The vision algorithms are designed for high-resolution front-view face and eye images (e.g., the height of the face is over 60% of image height in input images over 640 × 480 pixels).

DISADVANTAGES OF EXISTING SYSTEM:

• Existing method configuration is not applicable for buses and large vehicles.

• A bus mostly has a large front glass window to let the driver have awide-field-view of scene for safe driving since it is much wider than cars. Placing a camera on the front glass window is not practical, and that also blocks the drivers’ view.

• If the camera is installed on the frame around the window, the camera is not able to capture the frontal view of driver’s face, so that existing vision algorithms are not applicable.

PROPOSED SYSTEM:

The main contribution of this paper is a novel vision-based system for bus driver fatigue detection which is applicable to low-resolution face images captured from an oblique viewing angle to the driver’s face, so that it can share a wide-view camera mounted for driver’s full body behavior monitoring. The technological contributions can be summarized as follows:

• A novel framework for vision-based driver fatigue detection which integrates head-shoulder detection, multi-pose face detection, multi-model eye detection, eye openness estimation, fusion, and PERCLOS estimation for driver fatigue detection;

• A manifold learning algorithm to learn a mapping from a low-resolution eye image to a continuous level of eye openness;

• A fusion algorithm to obtain an accurate and robust eye openness estimate based on adaptive integration on multimodel eye detections on both eyes;

• A refined approach to compute PERCLOS measure based on the continuous levels of eye-openness.

ADVANTAGES OF PROPOSED SYSTEM:

• Our proposed method is able to distinguish the simulated drowsy and sleepy states from the normal state of driving on the low resolution images of faces and eyes observed from an oblique viewing angle.

• Hence, our system might be able to effectively monitor bus driver’s attention level without extra requirement for cameras. Our approach could extend the capability and applicability of existing vision-based techniques for driver fatigue detection.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System           : Pentium Dual Core.

• Hard Disk      : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB

• Tool                       : MATLAB R2013A

REFERENCE:

Bappaditya Mandal, Liyuan Li, Gang Sam Wang, and Jie Lin, “Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State”, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017.