Real-Time Video Processing using Contour Numbers and Angles for Non-urban Road Marker Classification

Abstract

Road users make vital decisions to safely maneuver their vehicles based on the road markers, which need to be correctly classified. The road markers classification is significantly important especially for the autonomous car technology. The current problems of extensive processing time and relatively lower average accuracy when classifying up to five types of road markers are addressed in this paper. Two novel real time video processing methods are proposed by extracting two formulated features namely the contour number, , and angle, 𝜃 to classify the road markers. Initially, the camera position is calibrated to obtain the best Field of View (FOV) for identifying a customized Region of Interest (ROI). An adaptive smoothing algorithm is performed on the ROI before the contours of the road markers and the corresponding two features are determined. It is observed that the achievable accuracy of the proposed methods at several non-urban road scenarios is approximately 96% and the processing time per frame is significantly reduced when the video resolution increases as compared to that of the existing approach.

Advances in lane marking detection algorithms for all-weather conditions

“…Based on the five common types of lane markers on the road [12][13][14], the number of contour lines ranges from zero to four. For example, the number of contour lines for marker S is two and D is four.…”

“…An accuracy value of 100% is recorded in two different video clips, which are Clip1 and Clip2Test. The proposed method has also been tested with different datasets including those applied in [12][13][14]47…”

“…These are the most common types of lane markers and the easiest ones to be classified. A few lane marker classification schemes have been proposed in literature to detect the more challenging types of lane markers, which are DS and SD such as in [12,47,50]. Due to the difficulty in detecting and differentiating DS and SD, the accuracy of classifying these lane markers is typically lower than 90%.…”

“…Due to the difficulty in detecting and differentiating DS and SD, the accuracy of classifying these lane markers is typically lower than 90%. Our previous method in detecting five lane markers including DS and SD have been demonstrated to increase the accuracy between 94% to 100% [12], [14] in normal weather. With effective pre-processing techniques, this method can be further improved to be applied in foggy and rainy weather.…”

“…Lane marker classification is an essential part of the lane detection mechanisms [11,12] to assist the drivers making the right decisions as well as to enhance the advanced driver assistance systems. A lane marker classification method using contour analysis has been proposed in [13,14].…”