Road sign detection method based on fast HDR image generation technique

Vavilin, Andrey; Deb, Kaushik; Kim, Tae Hee; Jo, K-H.

doi:10.1109/ivcnz.2010.6148848

Cited by 1 publication

(1 citation statement)

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“…In the past two decades, many approaches for traffic sign recognition have been proposed [5] [6]. However, based on our survey, mainly, most of them consist of three main stages: (1) Region segmentation for extracting the candidate region of signs.…”

Section: Introductionmentioning

confidence: 99%

Traffic sign recognition system for autonomous vehicle using cascade SVM classifier

Wahyono

Kurnianggoro

Hariyono

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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In past two decades, developing a system that can navigate vehicle autonomously becomes more interesting problem. The vehicle is equipped by sensors, such as radar, laser, GPS, and camera for sensing the surrounding. Among them, utilization of camera with computer vision technique is the most adopted method for constructing such a system. It is because camera provides a lot of information and is low-cost device rather than other sensors. Traffic road sign, as one of the important information from camera, carries a lot of useful information that are required for navigating. Thus, in this work, traffic sign detection and recognition is addressed. First, the input image is converted into normalize red and blue color space, as traffic sign usually appear with red and blue color. Second, maximally extremal stable region is then performed for extracting candidate region. Using heuristic rule of geometry properties, the false region will be excluded. Third, histogram of oriented gradient method is applied in order to extract feature from candidate region. Lastly, cascade support vector machine classifier is then processed to classify region belong to certain class of traffic sign. The extensive experiment would be carried out over German traffic sign recognition database and video. The experimental results demonstrate the effectiveness of our systems.

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Section: Introductionmentioning

confidence: 99%