Abstract :
The objective of this research is to develop a vision-based driver assistance system to enhance the driver´s safety in the nighttime. The proposed system performs both lane detection and vehicle recognition. In lane detection, three features including lane markers, brightness, slenderness and proximity are applied to detect the positions of lane markers in the image. On the other hand, vehicle recognition is achieved by using an evident feature which is extracted through three four steps: taillight standing-out process, adaptive thresholding, centroid detection, and taillight pairing algorithm. Besides, an automatic method is also provided to calculate the tilt and the pan of the camera by using the position of vanishing point which is detected in the image by applying Canny edge detection, Hough transform, major straight line extraction and vanishing point estimation. Experimental results for thousands of images are provided to demonstrate the effectiveness of the proposed approach in the nighttime. The lane detection rate is nearly 99%, and the vehicle recognition rate is about 91%. Furthermore, our system can process the image in almost real time.
Keywords :
Hough transforms; driver information systems; edge detection; feature extraction; night vision; Canny edge detection; Hough transform; adaptive thresholding; centroid detection; driver assistance system; feature extraction; lane detection; night vision; straight line extraction; taillight pairing; taillight standing-out process; vanishing point detection; vehicle recognition; Brightness; Cameras; Computer vision; Feature extraction; Image edge detection; Night vision; Safety; Vehicle detection; Vehicle driving; Vehicles; Driver Assistance System; Lane Detection; Night Vision; Vanishing Point Detection; Vehicle Recognition;
