Contour Sparse Representation using Voting Based Matching Pursuit for Road Marking Recognition

• Published in: IEEE access Vol. 7; p. 1
• Main Authors: Qian, Lisi, Fang, Bin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Computer vision; Contour matching; Dictionaries; dictionary learning; generalized Hough transform; Hough transformation; Machine learning; Marking; Matched pursuit; Matching pursuit algorithms; Optimization; Recognition; Road marking recognition; Roads; Scene analysis; Template matching; Texture; Training; Transforms; Voting; voting based matching pursuit
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2944484

The road marking recognition is an important part of road scene understanding based on computer vision. In road marking recognition, the contour-based algorithm such as generalized Hough transform (GHT) is more effective than the texture-based algorithm for no texture noise effect. However, there are a lot of redundant calculations in template matching which reduces the system efficiency. This paper presents a voting based matching pursuit (VMP) algorithm for locating reference points automatically in solving the sparse optimization problem, which achieves automatic alignment of samples in recognition. Then a dictionary learning method based on VMP algorithm is proposed, which uses a simple strategy to update the dictionary elements. The experimental results from two data sets have shown that the system efficiency is significant improved while ensuring the accuracy rate by the proposed method.