On using the hough transform for driving assistance applications

Hardzeyeu, Valiantsin; Klefenz, F.

doi:10.1109/iccp.2008.4648359

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…This kind of fatigue detection method is based on tracking vehicle distance, lane keeping and steering control behavior to indirectly detect the driver's fatigue. Hardzeyeu [31]judged the driver's fatigue through the vehicle driving behavior model. YoungjaeKim [32] [32] obtained the driver's fatigue by some features such as the speed, steering wheel angle, State of the accelerator and the driver's foot pedals.…”

Section: Contact Measurement Such As Electrocardiogram(ecg)mentioning

confidence: 99%

Smart car care systems and its technology prospects with service robots function

Zhang

et al. 2014

2014 IEEE International Conference on Information and Automation (ICIA)

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Social and human demands promote the development of automobile technology. For general vehicles, the auxiliary driving or automated driving is from a safe and comfortable level to solve human driving requirements. As the growth of the social demand for cultural and health care, the demand is bound to be reflected during the vehicle driving or proposed by consumers. It is important to consider the driver who may be in fatigue, illness and disability. In this paper, the concept of service robots in the car environment is proposed from car security, cultural and health care. The characteristics of the intelligent service robot system and core technology are discussed and the design scheme is also proposed.

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Section: Contact Measurement Such As Electrocardiogram(ecg)mentioning

confidence: 99%

Smart car care systems and its technology prospects with service robots function

Zhang

et al. 2014

2014 IEEE International Conference on Information and Automation (ICIA)

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“…One of the most common methods that use the information about shape is Hough Transform [11]. The main idea of Hough transform detection method is using a special Hough space, which is actually an accumulator image with voices for the sought-for objects in each pixelHaving transformed the original image, the Hough space image contains one or several brightest points with the largest amount of voices for the objects coordinates [12]. For example, in paper [13], a Hough-like transform is used to detect the U.S. speed limit signs.…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

Traffic Sign Detection on GPU using Color Shape Regular Expressions

Никоноров¹,

Petrov²,

Yakimov³

2013

Proceedings of the 4th International Workshop on Image Mining. Theory and Applications

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Regular expression matching on GPU is state-of-the-art technique for the processing of a huge amount of the text information, for example, in network activity analyzers and intrusion prevention systems. This paper proposes a fast and robust algorithm for traffic sign detection, which is based on color shape regular expressions matching through the image pixels. We consider a fast massively-parallel GPU implementation of the color shape regular expression, using deterministic finite automaton and special case of non-deterministic finite automaton. The performance of the proposed localization algorithm is compared with the Hough transform, which is commonly used for traffic sign recognition.

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“…In vehicle engineering, it has been more present in autonomous driving tasks such as lane assist and traffic sign recognition. [13][14][15] The data base for the implementation and validation of the separation procedure are measurements of eleven vehicles, divided in four hybrid vehicles and seven pure electric vehicles. A variety of vehicle types and characteristic factors was considered to develop a robust method for future use of the developed method on a variety of other vehicles.…”

Section: Introductionmentioning

confidence: 99%

Automated inverter noise separation in the interior of electric vehicles using adapted Kirsch-Compass kernel and Hough-Transformation

Fröhlingsdorf

Pischinger

Günther

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The increasing electrification of the powertrain of road vehicles creates new NVH challenges. In electric vehicles, the inverter noise is a dominant component of the overall interior noise. The typical high-frequency tonal noise shares often have a negative impact on the perceived sound quality of the vehicle. To improve the acoustic optimization, which is a complex and time-consuming process, a novel method for the automated separation of inverter noise shares in the vehicle interior has been developed and is presented in this paper. This method is based on image processing such as Kirsch-Compass filter and Hough-Transformation for line detection. It is designed to work even without the knowledge about component parameters such as the switching frequency and the number of pole pairs of the electric machine. The detection rate of the switching frequencies in the vehicle interior is 94%, and up to 86% for the inverter harmonics.

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On using the hough transform for driving assistance applications

Cited by 19 publications

References 16 publications

Smart car care systems and its technology prospects with service robots function

Smart car care systems and its technology prospects with service robots function

Traffic Sign Detection on GPU using Color Shape Regular Expressions

Automated inverter noise separation in the interior of electric vehicles using adapted Kirsch-Compass kernel and Hough-Transformation

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