Suspended traffic lights detection and distance estimation using color features

Diaz, Moises; Cerri, Pietro; Medina, Javier

doi:10.1109/itsc.2012.6338765

Cited by 67 publications

(30 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There will be two modes, a current mode and a predicted mode. If the image captured is a red light, then the system will be able to predict that the next mode will be a green light in x seconds [8].Also using ML the proposed system will be able to detect and distinguish between 2 different traffic signal which are at a certain distance from each other and will consider the one closest to it [3].…”

Section: Future Workmentioning

confidence: 99%

Driver Assistance System using in-vehicle Traffic Lights and Signs

Jain¹,

Tanay²,

Gangele³

et al. 2018

IJET

View full text Add to dashboard Cite

In recent years, with the advancement of vehicular communication, it is possible to detect various road signs and provide traffic light information to the driver inside the vehicle with the application of heads-up display (HUD). It detects road signs, does basic classifications and accordingly directs the driver to slow down or stop the vehicle. The vehicle's heads-up display keeps the driver focused by providing road warnings, speed limit, traffic signals and some vital navigation information in the driver's line of sight(LOS). This system has 4 phases, Image recognition, wireless communication, obstacle detection and driver mechanism. This system aims to create a prototype of a smart driver assistance system which provides better road traffic and driver's safety in countries with high traffic congestion where fully automated vehicles cannot function effectively. This system can be easily implemented in real time scenarios to reduce accidents and enhance the convenience of driving.

show abstract

Section: Future Workmentioning

confidence: 99%

Driver Assistance System using in-vehicle Traffic Lights and Signs

Jain¹,

Tanay²,

Gangele³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…In conventional traffic light detection, a candidate set of TL is, typically, generated by colour and shape information [8], [7], [2], [9]. This set is then pruned by tracking and spatial information, before identifying the TL state.…”

Section: Related Workmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…However, the color model is quite diverse due to algorithm used by the researchers. Normalized RGB to reduce the effect from changing lighting conditions and recognizing TLs using circular Hough transform is used by [1], while using the same color model adds background condition and estimates optimal distance from car to traffic light for detection [2]. However, the RGB color model is depends on lighting conditions.…”

Section: Related Workmentioning

confidence: 99%

“…4(c)) to extract both of red and yellow TL in one channel by using Eq. (2). Then, the result of this step is given and shown in Fig.…”

Section: A Traffic Light Extractionmentioning

confidence: 99%

Red traffic light detection using fast radial symmetry transform

Sooksatra

Kondo

2014

2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

View full text Add to dashboard Cite

Traffic light is one of the important signs for drivers that help managing the car flow and reducing accident on the road. As of today technology, there exists a traffic light detection system that warns the driver to reduce the accident significantly. In this paper, we are concerned with only the red and yellow traffic light to reduce false positive and time consumption. The fast radial symmetry transform is a fast variation of the circular Hough transform. The CIELab color model is used because it is less dependent on lighting conditions. The precision and the recall of the proposed method are increased from previous work.

show abstract

Suspended traffic lights detection and distance estimation using color features

Cited by 67 publications

References 8 publications

Driver Assistance System using in-vehicle Traffic Lights and Signs

Driver Assistance System using in-vehicle Traffic Lights and Signs

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

Red traffic light detection using fast radial symmetry transform

Contact Info

Product

Resources

About