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• W2516801572 abstract "As the around-view monitoring (AVM) system becomes one of the essential components for advanced driver assistance systems (ADAS), many applications using AVM such as parking guidance system are actively being developed. As a key step for such applications, detecting road markings robustly is a very important issue to be solved. However, compared to the lane marking detection methods, detection of non-lane markings, such as text marks painted on the road, has been less studied so far. While some of methods for detecting non-lane markings exist, many of them are restricted to roadways only, or work poorly on AVM images. In this paper, we propose an algorithm which can robustly detect non-lane road markings on AVM images. We first propose a difference-of-Gaussian based method for extracting a connected component set, followed by a novel grouping method for grouping connected components based on convexity condition. For a classification task, we exploit the Random Forest classifier. We demonstrate the robustness and detection accuracy of our methods through various experiments by using the dataset collected from various environments." @default.
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• W2516801572 date "2016-06-01" @default.
• W2516801572 modified "2023-09-23" @default.
• W2516801572 title "Robust road marking detection using convex grouping method in around-view monitoring system" @default.
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• W2516801572 doi "https://doi.org/10.1109/ivs.2016.7535511" @default.
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