Vision-Based Vehicle Speed Measurement Method

Czajewski, Witold; Iwanowski, Marcin

doi:10.1007/978-3-642-15910-7_35

Cited by 17 publications

(24 citation statements)

References 14 publications

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“…In [21], the authors used the background/foreground segmentation technique to detect moving vehicles and blob tracking method to estimate their speed. Some other approaches like [22][23][24] used vehicles' license plates for detection purposes and by tracking the extracted features from the license plates, their speed was measured. In [22] detected characters using an Optical Character Recognition (OCR) algorithm which is inconstant in position and size are used for vehicle detection.…”

Section: B Related Workmentioning

confidence: 99%

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Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Afshany¹,

Tourani²,

Shahbahrami³

et al. 2019

IJISA

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Nowadays, Intelligent Transportation Systems (ITS) are known as powerful solutions for handling traffic-related issues. ITS are used in various applications such as traffic signal control, vehicle counting, and automatic license plate detection. In the special case, video cameras are applied in ITS which can provide useful information after processing their outputs, known as Video-based Intelligent Transportation Systems (V-ITS). Among various applications of V-ITS, automatic vehicle speed measurement is a fast-growing field due to its numerous benefits. In this regard, visual appearancebased methods are common types of video-based speed measurement approaches which suffer from a computationally intensive performance. These methods repeatedly search for special visual features of vehicles, like the license plate, in consecutive frames. In this paper, a parallelized version of an appearance-based speed measurement method is presented which is real-time and requires lower computational costs. To acquire this, datalevel parallelism was applied on three computationally intensive modules of the method with low dependencies using NVidia's CUDA platform. The parallelization process was performed by the distribution of the method's constituent modules on multiple processing elements, which resulted in better throughputs and massively parallelism. Experimental results have shown that the CUDA-enabled implementation runs about 1.81 times faster than the main sequential approach to calculate each vehicle's speed. In addition, the parallelized kernels of the mentioned modules provide 21.28, 408.71 and 188.87 speed-up in singularly execution. The reason for performing these experiments was to clarify the vital role of computational cost in developing video-based speed measurement systems for real-time applications.

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Section: B Related Workmentioning

confidence: 99%

“…This method requires a robust OCR and does not provide acceptable results even in a controlled environment. Similar work in [23] is done based on vehicles' license plate detection and tracking.…”

Section: B Related Workmentioning

confidence: 99%

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Afshany¹,

Tourani²,

Shahbahrami³

et al. 2019

IJISA

View full text Add to dashboard Cite

show abstract

“…There is an extensive literature on vehicle speed estimation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Most approaches are based on specialized speed sensors [6], or are dedicated to specific contexts, such as speed estimation from vehicle's headlight in night scenes [7,8], speed estimation using the signal from mobile telecommunication network [9], or speed estimation from a car-mounted camera to avoid collisions [10].…”

Section: Related Workmentioning

confidence: 99%

“…In 2010, Czajewski and Iwanowski [19] proposed a speed measurement method based on license plate recognition. They used an elementary license plate recognition algorithm based on an adaptive threshold of the input image, followed by a contour extraction and a character grouping step.…”

Section: Vehicle Speed Estimationmentioning

confidence: 99%

Vehicle speed estimation by license plate detection and tracking

Luvizon

Nassu

Minetto

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…This task needs to be performed with a high reliability to avoid any collision. Various approachs have been tried for this, many of then based on vision systems [1][2][3][4][5][6]. Unfortunately, a vision system suffers leverage of light, mist and others environmental influences, which can affect the reliability of the system.…”

Section: Introductionmentioning

confidence: 99%