Adaptive Regions of Interest Based on HSV Histograms for Lane Marks Detection

Abstract

The lane detection is a vital component of autonomous vehicle systems. Although many different approaches have been proposed in the literature it is still a challenge to correctly identify road lane marks under abrupt light variations. In this work a vision-based ego-lane detection system is proposed with the capability of automatically adapting to abrupt lighting changes. The proposed method automatically adjusts the feature extraction and salient point tracking cues introduced by the GOLDIE (Geometric Overture for Lane Detection by Intersections Entirety) algorithm. The variance of the lighting conditions is measured using hue-saturation histogram and abrupt light changes on the road are detected based on the difference between histograms. Experimental comparison with previously proposed algorithms demonstrated that this method achieved efficient lane detection in the presence of shadows and headlights. In particular, the accuracy of the algorithm applied on the footage with highest light variation increased 12.5% on average. The overall detection rate increased 4%, which illustrated the applicability of the method.

Bayesian curved lane estimation for autonomous driving

“…Other approaches are based on color features and HT for the sake of efficiency. In Bottazzi et al (2014), the authors introduced a new method for lane marking detection based on HSV (Hue, Value, Saturation) histograms. A dynamic region of interest (ROI) is determined using an earlier triangle model.…”

Raspberry-Pi Based Real Time Lane Departure Warning System using Image Processing

“…To expand the lane identification precision of the method various pre-processing filter are used by the researchers. The methods like hybrid median filter [7], kalman filter [5], Gaussian filter [8], segmentation [10] and gabor filter [14]. In this paper, the canny edges detector is used to detect the edges from the blur images.…”

“…In reference [5] author used edges feature extraction and grouping along with the Hough transform but it fails to detect lane in heavy traffic and confusing road textures. In reference [10] author used segmentation method to detect the lane but gives high false positive rate. The straight and curves roads lanes are detected using the Hough transform and under various illuminating conditions Hough Transform shows good performance.…”

Review of Lane Detection and Tracking Algorithms in Advanced Driver Assistance System

“…Bottazzi et al, [19] proposes a histogram based illumination invariant lane detection method. A dynamic region of interest (DROI) is defined using a prior triangle model.…”