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Horizontal line detection using genetic algorithm

Uuganbayar Ganbold, Takuya Akashi, Chao Zhang, Hiroyuki Tomita, Hitoshi Kubota

Year
2019
Citations
2

Abstract

In mobile robots and autonomous vehicles, the information of depth and camera’s orientation are necessary for the applications of positioning and navigation. In this paper, we propose an appearance-assumption-based method for the detection of horizontal line (HL) or vanishing line (VL) on the image observed with a single omnidirectional camera. HL can provide the important information about depth and camera’s orientation for the single view estimation. The detection of HL is assumed as an optimization problem. The optimization of the line is adopted by the genetic algorithm (GA). We implement the real-time detection method of HL using GA. Therefore, the proposed method is evaluated on the multiple videos with the various environment. Experimental results demonstrate the efficiency of the proposed method.

Keywords

Computer visionComputer scienceArtificial intelligenceLine (geometry)Orientation (vector space)Genetic algorithmMobile robotOmnidirectional cameraVanishing pointLine segment

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