Traffic Light Detection Using Rotated Principal Component Analysis for Video-Based Car Navigation System

Joo, Sung Kwan; Kim, Yongkwon; Cho, Seong Ik; Choi, Kyoung-Ho; Lee, Kisung

doi:10.1093/ietisy/e91-d.12.2884

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Kimura et al proposed a method for detecting traffic lights that uses a color histogram [15] and Yelal et al [16] presented a method for tracking the color of traffic lights with contour tracking. Joo et al proposed a method using rotated principal component analysis [17]. Park and Jeong's traffic light detection system uses color clustering and a circularity check [18].…”

Section: Introductionmentioning

confidence: 99%

Traffic Light Detection Considering Color Saturation Using In-Vehicle Stereo Camera

Moizumi

Sugaya

Omachi

et al. 2016

Journal of Information Processing

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One of the major causes of traffic accidents according to the statistical report on traffic accidents in Japan is the disregard of traffic lights by drivers. It would be useful if driving support systems could detect and recognize traffic lights and give appropriate information to drivers. Although many studies on intelligent transportation systems have been conducted, the detection of traffic lights using images remains a difficult problem. This is because traffic lights are very small as compared to other objects and there are many objects similar to traffic lights in the road environment. In addition, the pixel colors of traffic lights are easily over-saturated, which renders traffic light detection using color information difficult. The rapid deployment of the new LED traffic lights has led to a new problem. Since LED lights blink at high frequency, if they are captured by a digital video camera, there are frames in which all the traffic lights appear to be turned off. It is impossible to detect traffic lights in these frames by searching the ordinary color of traffic lights. In this paper, we focus on the stable detection of traffic lights, even when they are blinking or when their colors are over-saturated. A method for detecting candidate traffic lights utilizing intensity information together with color information is proposed for handling over-saturated pixels. To exclude candidates that are not traffic lights efficiently, the sizes of the detected candidates are calculated using a stereo image. In addition, we introduce tracking with a Kalman filter to avoid incorrect detection and achieve stable detection of blinking lights. The experimental results using video sequences taken by an in-vehicle stereo camera verify the efficacy of the proposed approaches.

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Section: Introductionmentioning

confidence: 99%

Traffic Light Detection Considering Color Saturation Using In-Vehicle Stereo Camera

Moizumi

Sugaya

Omachi

et al. 2016

Journal of Information Processing

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“…Accurate and real-time detection of vehicle position, speed and traffic flows are important issues for driving assistance systems and traffic surveillance systems [1]- [4]. During the detection, errors often arise because of camera vibration and constraints such as the limitations of image resolution, quantization errors, and lens distortions [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Lin

Chen

2009

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents new approaches for the estimation of range between the preceding vehicle and the experimental vehicle, estimation of vehicle size and its projective size, and dynamic camera calibration. First, our proposed approaches adopt a camera model to transform coordinates from the ground plane onto the image plane to estimate the relative position between the detected vehicle and the camera. Then, to estimate the actual and projective size of the preceding vehicle, we propose a new estimation method. This method can estimate the range from a preceding vehicle to the camera based on contact points between its tires and the ground and then estimate the actual size of the vehicle according to the positions of its vertexes in the image. Because the projective size of a vehicle varies with respect to its distance to the camera, we also present a simple and rapid method of estimating a vehicle's projective height, which allows a reduction in computational time for size estimation in real-time systems. Errors caused by the application of different camera parameters are also estimated and analyzed in this study. The estimation results are used to determine suitable parameters during camera installation to suppress estimation errors. Finally, to guarantee robustness of the detection system, a new efficient approach to dynamic calibration is presented to obtain accurate camera parameters, even when they are changed by camera vibration owing to on-road driving. Experimental results demonstrate that our approaches can provide accurate and robust estimation results of range and size of target vehicles.

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Traffic light mapping, localization, and state detection for autonomous vehicles

Levinson

Askeland

Dolson

et al. 2011

2011 IEEE International Conference on Robotics and Automation

121

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Traffic Light Detection Using Rotated Principal Component Analysis for Video-Based Car Navigation System

Cited by 3 publications

References 7 publications

Traffic Light Detection Considering Color Saturation Using In-Vehicle Stereo Camera

Traffic Light Detection Considering Color Saturation Using In-Vehicle Stereo Camera

Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems

Traffic light mapping, localization, and state detection for autonomous vehicles

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