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, π 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.
Collaboration network of the authors
15 authors Β·
45 co-authorship links
Β· 8 across different organisations
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Multimedia University
Universiti Teknikal Malaysia Melaka
University of Technology Malaysia
Affiliation unknown
Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles Β· bigger circle = more articles
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