Traffic light detection during day and night conditions by a camera

Yu, Chunhe; Huang, Chuan; Yao, Lina

doi:10.1109/icosp.2010.5655934

Cited by 35 publications

(17 citation statements)

References 6 publications

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“…To detect the TL, vision-based ADAS are commonly used [2], [3], [7], [10], [11], with the most important challenges occurring due to illumination variation, occlusions, and background noises. The background noises include traffic signs, billboards, vehicle tail-lights, etc.…”

Section: Introductionmentioning

confidence: 99%

Traffic light recognition in varying illumination using deep learning and saliency map

John

Yoneda

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

105

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The accurate detection and recognition of traffic lights is important for autonomous vehicle navigation and advanced driver aid systems. In this paper, we present a traffic light recognition algorithm for varying illumination conditions using computer vision and machine learning. More specifically, a convolutional neural network is used to extract and detect features from visual camera images. To improve the recognition accuracy, an on-board GPS sensor is employed to identify the region-of-interest, in the visual image, that contains the traffic light. In addition, a saliency map containing the traffic light location is generated using the normal illumination recognition to assist the recognition under low illumination conditions. The proposed algorithm was evaluated on our data sets acquired in a variety of real world environments and compared with the performance of a baseline traffic signal recognition algorithm. The experimental results demonstrate the high recognition accuracy of the proposed algorithm in varied illumination conditions.

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Section: Introductionmentioning

confidence: 99%

Traffic light recognition in varying illumination using deep learning and saliency map

John

Yoneda

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

105

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“…In detection, color/lightness information is widely used [1] [2] [3] [4] [5] [6], because the special colors of the traffic lights and their high brightness are the most prominent image features.…”

Section: Introductionmentioning

confidence: 99%

A framework of traffic lights detection, tracking and recognition based on motion models

Zhang

Yang

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Detection of traffic lights is a basic technology for autonomous vehicle and driver assistant system. This paper presents a framework of detection, tracking, classification and online mapping using the images captured by a camera mounted on the vehicle and the position and attitude information from GPS/INS. The sequential results of detection, which is treated as observations with uncertainty, are associated with the targets in previous frame. The results of association are filtered and classified. In addition, the target position in the image is predicted based on a novel motion model and aided by a online mapping module that provides the model with 3D location information. The precise motion model significantly improves the performance of the association. The prediction algorithm based on our motion model is evaluated and compared with the other methods.

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“…An easy way to detect traffic lights is shown javier.sanchez.medina at ieee.org in [1]. The authors present a system based on traffic light features, such as color, area and density of pixels.…”

Section: A State Of Artmentioning

confidence: 99%

Suspended traffic lights detection and distance estimation using color features

Diaz¹,

Cerri

Medina

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract-Traffic Light Detection is a problem differently approached by many research groups around the world. Here we present a novel technique to detect suspended traffic lights, based on colors and features such as black area of traffic lights or area of lighting lamps. Additionally, the traffic light distance is estimated aiming at slowing down and stopping in the correct position, in case of red light. Some preliminary test results are presented to assess both the detection rate and the distance estimation.

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Traffic light detection during day and night conditions by a camera

Cited by 35 publications

References 6 publications

Traffic light recognition in varying illumination using deep learning and saliency map

Traffic light recognition in varying illumination using deep learning and saliency map

A framework of traffic lights detection, tracking and recognition based on motion models

Suspended traffic lights detection and distance estimation using color features

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