A Novel Learning-Based Method for Lane Line Detection and Tracking

• Main Authors: Ko, Ho-Chang, 柯和昌
• Other Authors: Sun, Chuen-Tsai
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/97871880855902544924

碩士 === 國立交通大學 === 資訊學院資訊學程 === 104 === In recent years, traffic accidents are happening frequently. Most accidents occurred are due to improper driving behavior such as driver’s distraction, drowsy driving, or not focusing on traffic. Therefore, how to use modern technology to make drivers driving more safely becomes more and more important. The most common technology is Lane Departure Warning System (LDWS). By fast computer processing, the vehicle is able to recognize where the lane line position and knows whether it is safe now. If lane departure happens, the system can call driver’s attention back to traffic with warnings to prevent car accident. Lane detection is the most important part of LDWS. This thesis tries to detect lane lines through a learning analysis with captured images from a single-lens camera facing toward vehicle front. In our proposed system, it starts with image pre-processing such as images resizing, gray scaling, sharpness and equalization for every frame. Using edge detection to extract edge features and Haar-like feature cascade classifier with Adaboost to detect lane lines. Finally, to speed up analysis and improve the accuracy of analysis results by reducing the range and data amount using simple lane tracking with record and motion vector prediction. Experimental results show that our proposed learning-based lane detection method is able to detect the lane lines accurately, especially in some scenes, such as nighttime, roads with shadow of objects, or complex lane markings.