Real-time traffic sign detection and classification method for intelligent vehicles

Kaplan, Kemal; Kurtul, Caner; Akın, H. Levent

doi:10.1109/icves.2012.6294316

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Feature extraction is used widely for addressing research challenges related to image detection. (Benco et al, 2014;Kaplan et al, 2012;Mingqiang et al, 2008;Qian et al, 2015;Vashisht & Kumar, 2021a, 2021bWang et al, 2013;Zhang et al, 2017). In this research work, HSV…”

Section: Colour and Shape Featuresmentioning

confidence: 80%

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Vashisht

Kumar

2021

Expert Systems

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In recent past, emerging technology, such as AI has revolutionized progress and advancement of traffic management solutions in context of smart cities. This paper describes a novel approach of using 3D colour-texture based feature for image detection on public datasets of Chinese traffic sign research database (TSRD) and Mapillary image database. The implementation of 3D colour texture feature for traffic sign detection is evaluated using artificial neural network and multiple other machine learning algorithms as classifier for image detection purposes. Both datasets used in our experiments are considered among most diverse traffic signage datasets globally with annotations of almost all classes. Image datasets have been publicly made available to researchers for academic purposes. For classification of traffic sign images on Chinese TSRD and Mapillary datasets, the result outcome from backpropagation neural network (NN) classifier outperforms all other and produced best results and under multiple ML algorithms, support vector machines (SVM) cubic has best results. For classification of traffic sign images on Mapillary dataset, the result outcome from rational quadratic Gaussian process regression has best results. Also, the comparisons of results with other similar novel works have also been discussed.The proposed approach outperformed the previous experiments as observed through comparative analysis performed on both datasets. The conclusion of the research work highlights that the image detection performance is noticeably improved by using the combined 3D colour-texture feature based proposed approach.

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Section: Colour and Shape Featuresmentioning

confidence: 80%

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Vashisht

Kumar

2021

Expert Systems

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“…Visibility can be impacted by the factors such as atmospheric conditions and the blind spots [24]. Enhancing driver attention and minimizing the size of the blind spots of cars can help overcome lack of visibility issues [25]. Another way to monitor drowsiness is proposed by integrating intelligent control systems into vehicles to include the human driver control loop [22].…”

Section: The Physical and Cognitive Context Of A Lane Changementioning

confidence: 99%

Reducing Side-Sweep Accidents with Vehicle-to-Vehicle Communication

Bulumulle

Bölöni

2016

JSAN

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Side-sweep accidents are one of the major causes of loss of life and property damage on highways. This type of accident is caused by a driver initiating a lane change while another vehicle is blocking the road in the target lane. In this article, we are trying to quantify the degree to which different implementations of vehicle-to-vehicle (V2V) communication could reduce the occurrence of such accidents. We present the design of a simulator that takes into account common sources of lack of driver awareness such as blind-spots and lack of attention. Then, we study the impact of both traditional, non-technological communication means such as turning signals as well as unidirectional and bidirectional V2V communications.

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Implementation of Artificial Neural Network for Image Recognition Using Chinese Traffic Sign Image Dataset

Vashisht

Kumar

2021

Lecture Notes in Networks and Systems

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Real-time traffic sign detection and classification method for intelligent vehicles

Cited by 6 publications

References 20 publications

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Reducing Side-Sweep Accidents with Vehicle-to-Vehicle Communication

Implementation of Artificial Neural Network for Image Recognition Using Chinese Traffic Sign Image Dataset

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