Driver Fatigue Detection Based on Eye

Abstract

This project describes an efficient method for drowsiness detection by three well defined phases. These three phases are facial features detection using Viola Jones, the eye tracking and yawning detection. Once the face is detected, the system is made illumination invariant by segmenting the skin part alone and considering only the chromatic components to reject most of the non face image backgrounds based on skin color. The tracking of eyes and yawning detection are done by correlation coefficient template matching. The feature vectors from each of the above phases are concatenated and a binary linear support vector machine classifier is used to classify the consecutive frames into fatigue and non fatigue states and sound an alarm for the former, if it is above the threshold time. Extensive real time experiments prove that the proposed method is highly efficient in finding the drowsiness and alerting the driver.

Existing System

In the existing system, intelligent systems was developed to prevent car accidents can be very effective in minimizing accident death toll. One of the factors which play an important role in accidents is the human errors including driving fatigue relying on new smart techniques; this project detects the signs of fatigue and sleepiness in the face of the person at the time of driving. The proposed system is based on three separate algorithms. In this model, the person face is filmed by a camera in the first step by receiving 15fps video sequence. Then, the images are transformed from RGB space into YCbC r and HS V spaces. The face area is separated from other parts and highly accurate HDP is achieved. That the eyes are open or closed in a specific time interval is determined by focusing on thresholding and equations concerning the symmetry of human faces a finally using K-means Clustering, the frequency of yawning is identified.

Proposed System

Each year hundreds of people lose their lives due to traffic accidents around the world. The most important factor which helps detect driver fatigue is the state of eyes. In the proposed work, Sobel which is an edge detection method is preferred over others methods like Canny. The status of the eyes is determined in every frame using the correlation coefficient template matching method. The Sobel edge detection method is also used to detect the eyes’ precise and exact boundaries. The technique starts from left and right side, to find eyes, therefore can detect the eyes separately. To distinguish the fatigue, the eyes’ states should be recognized accurately. The Sobel edge detection method is also used to detect the eyes’ precise and exact boundaries. The technique starts from left and right side, to find eyes, therefore can detect the eyes separately. To distinguish the fatigue, the eyes’ states should be recognized accurately.

Architecture

ARCHITECTURE DIAGRAM