Traffic Lights Detection Based on Visual Attention and Spot-Lights Regions Detection

Kim, JongBae

doi:10.5573/ieie.2014.51.6.132

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Improved environmental perception is possible by attaching these sensors to the vehicle. However, these sensors increase the price of vehicles, and they frequently fail or need to be replaced due to slight accidents involving vehicle contact [9]. Therefore, if measures more effective than the current vision-sensor-based vehicle detection function can be applied to the ADAS system in various ways, more vehicle drivers will be able to benefit from safe driving support.…”

Section: Introductionmentioning

confidence: 99%

Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations

Kim¹

2019

Symmetry

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The number and range of the candidate vehicle license plate (VLP) region affects the result of the VLP extraction symmetrically. Therefore, in order to improve the VLP extraction rate, many candidate VLP regions are selected. However, there is a problem that the processing time increases symmetrically. In this paper, we propose a method that allows detecting a vehicle license plate in the real-time mode. To do this, the proposed method makes use of the region-based convolutional neural network (R-CNN) method and morphological operations. The R-CNN method is a deep learning method that selects a large number of candidate regions from an input image and compares them to determine whether objects of interest are included. However, this method has limitations when used in real-time processing. Therefore, to address this limitation in the proposed method, while selecting a candidate vehicle region, the selection range is reduced based on the size and position of the vehicle in the input image; hence, processing can be performed quickly. A vehicle license plate is detected by performing a morphological operation based on the edge pixel distribution of the detected vehicle region. Experimental results show that the detection rate of vehicles is approximately 92% in real road environments, and the detection rate of vehicle license plates is approximately 83%.

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Section: Introductionmentioning

confidence: 99%

Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations

Kim¹

2019

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Real Time Traffic Signal Recognition Using HSI and YCbCr Color Models and Adaboost Algorithm

Park

Lee²

2016

Transactions of the Korean Society of Automotive Engineers

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: This paper proposes an algorithm to effectively detect the traffic lights and recognize the traffic signals using a monocular camera mounted on the front windshield glass of a vehicle in day time. The algorithm consists of three main parts. The first part is to generate the candidates of a traffic light. After conversion of RGB color model into HSI and YCbCr color spaces, the regions considered as a traffic light are detected. For these regions, edge processing is applied to extract the borders of the traffic light. The second part is to divide the candidates into traffic lights and non-traffic lights using Haar-like features and Adaboost algorithm. The third part is to recognize the signals of the traffic light using a template matching. Experimental results show that the proposed algorithm successfully detects the traffic lights and recognizes the traffic signals in real time in a variety of environments.

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Traffic Lights Detection Based on Visual Attention and Spot-Lights Regions Detection

Cited by 2 publications

References 6 publications

Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations

Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations

Real Time Traffic Signal Recognition Using HSI and YCbCr Color Models and Adaboost Algorithm

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