This paper presents a robust approach for road marking detection and recognition from images captured by an embedded camera mounted on a car. Our method is designed to cope with illumination changes, shadows, and harsh meteorological conditions. Furthermore, the algorithm can effectively group complex multi-symbol shapes into an individual road marking. For this purpose, the proposed technique relies on MSER features to obtain candidate regions which are further merged using density-based clustering. Finally, these regions of interest are recognized using machine learning approaches. Worth noting, the algorithm is versatile since it does not utilize any prior information about lane position or road space. The proposed method compares favorably to other existing works through a large number of experiments on an extensive road marking dataset.
Publications
International Conference
Robust Road Marking Detection and Recognition Using Density-Based Grouping and Machine Learning Techniques
조회 수 5678
댓글 0
저 자 | Oleksandr Bailo, Seokju Lee, Francois Rameau, Jae Shin Yoon, In So Kweon |
---|---|
학 회 | Winter Conference of Applications on Computer Vision (WACV) |
논문일시(Year) | 2017 |
논문일시(Month) | 03 |
Prev Single Image Depth Estimation using Convolutional Neural Netw...
Single Image Depth Estimation using Convolutional Neural Netw...
2017.01.17by
Robot System of DRC‐HUBO+ and Control Strategy of Team KAIST in DARPA Robotics Challenge Finals Next
Robot System of DRC‐HUBO+ and Control Strategy of Team KAIST in DARPA Robotics Challenge Finals
2016.12.30by 관리자