A vision-based vehicle speed warning system

Shih Chieh Huang, Chien Chuan Lin, Ming-Shi Wang

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

In this paper, a vision based vehicle speed warning system was proposed which has been implemented on the PAC Duo embedded platform. The system consisted of a vehicle digital video recorder which used to capture the image in front of the vehicle, a hidden Markov model module to predict the next instant vehicle speed from the previous vehicle speeds, and a warning system to give the driver a warning signal when the predicted vehicle speed is larger than a predefined threshold value. The difficulty of the system is how to get the vehicle speed under the condition of without real vehicle speed provided. In this system, the vehicle speed provided for the hidden Markov model to predict the next instant vehicle speed is estimated from the images captured via the digital video recorder. Two methods have been evaluated for estimating the vehicle speed. One is using feature matching method between the two contiguous images; the other is via the pre-calculating distance matching of each pixel of the image. The testing speed range is from 0 to 110 km/hr and the processing frame rate is 5 fps. The accuracy of the speed estimation is the feature matching method: 8%; and the other method: 12%.

Original languageEnglish
Pages832-834
Number of pages3
DOIs
Publication statusPublished - 2012 Nov 28
Event9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012 - Fukuoka, Japan
Duration: 2012 Sep 42012 Sep 7

Other

Other9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012
CountryJapan
CityFukuoka
Period12-09-0412-09-07

Fingerprint

Alarm systems
Hidden Markov models
Pixels

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Huang, S. C., Lin, C. C., & Wang, M-S. (2012). A vision-based vehicle speed warning system. 832-834. Paper presented at 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012, Fukuoka, Japan. https://doi.org/10.1109/UIC-ATC.2012.110
Huang, Shih Chieh ; Lin, Chien Chuan ; Wang, Ming-Shi. / A vision-based vehicle speed warning system. Paper presented at 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012, Fukuoka, Japan.3 p.
@conference{6ad0e117ea2d44bcb35bd32d9f5a10fb,
title = "A vision-based vehicle speed warning system",
abstract = "In this paper, a vision based vehicle speed warning system was proposed which has been implemented on the PAC Duo embedded platform. The system consisted of a vehicle digital video recorder which used to capture the image in front of the vehicle, a hidden Markov model module to predict the next instant vehicle speed from the previous vehicle speeds, and a warning system to give the driver a warning signal when the predicted vehicle speed is larger than a predefined threshold value. The difficulty of the system is how to get the vehicle speed under the condition of without real vehicle speed provided. In this system, the vehicle speed provided for the hidden Markov model to predict the next instant vehicle speed is estimated from the images captured via the digital video recorder. Two methods have been evaluated for estimating the vehicle speed. One is using feature matching method between the two contiguous images; the other is via the pre-calculating distance matching of each pixel of the image. The testing speed range is from 0 to 110 km/hr and the processing frame rate is 5 fps. The accuracy of the speed estimation is the feature matching method: 8{\%}; and the other method: 12{\%}.",
author = "Huang, {Shih Chieh} and Lin, {Chien Chuan} and Ming-Shi Wang",
year = "2012",
month = "11",
day = "28",
doi = "10.1109/UIC-ATC.2012.110",
language = "English",
pages = "832--834",
note = "9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012 ; Conference date: 04-09-2012 Through 07-09-2012",

}

Huang, SC, Lin, CC & Wang, M-S 2012, 'A vision-based vehicle speed warning system', Paper presented at 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012, Fukuoka, Japan, 12-09-04 - 12-09-07 pp. 832-834. https://doi.org/10.1109/UIC-ATC.2012.110

A vision-based vehicle speed warning system. / Huang, Shih Chieh; Lin, Chien Chuan; Wang, Ming-Shi.

2012. 832-834 Paper presented at 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012, Fukuoka, Japan.

Research output: Contribution to conferencePaper

TY - CONF

T1 - A vision-based vehicle speed warning system

AU - Huang, Shih Chieh

AU - Lin, Chien Chuan

AU - Wang, Ming-Shi

PY - 2012/11/28

Y1 - 2012/11/28

N2 - In this paper, a vision based vehicle speed warning system was proposed which has been implemented on the PAC Duo embedded platform. The system consisted of a vehicle digital video recorder which used to capture the image in front of the vehicle, a hidden Markov model module to predict the next instant vehicle speed from the previous vehicle speeds, and a warning system to give the driver a warning signal when the predicted vehicle speed is larger than a predefined threshold value. The difficulty of the system is how to get the vehicle speed under the condition of without real vehicle speed provided. In this system, the vehicle speed provided for the hidden Markov model to predict the next instant vehicle speed is estimated from the images captured via the digital video recorder. Two methods have been evaluated for estimating the vehicle speed. One is using feature matching method between the two contiguous images; the other is via the pre-calculating distance matching of each pixel of the image. The testing speed range is from 0 to 110 km/hr and the processing frame rate is 5 fps. The accuracy of the speed estimation is the feature matching method: 8%; and the other method: 12%.

AB - In this paper, a vision based vehicle speed warning system was proposed which has been implemented on the PAC Duo embedded platform. The system consisted of a vehicle digital video recorder which used to capture the image in front of the vehicle, a hidden Markov model module to predict the next instant vehicle speed from the previous vehicle speeds, and a warning system to give the driver a warning signal when the predicted vehicle speed is larger than a predefined threshold value. The difficulty of the system is how to get the vehicle speed under the condition of without real vehicle speed provided. In this system, the vehicle speed provided for the hidden Markov model to predict the next instant vehicle speed is estimated from the images captured via the digital video recorder. Two methods have been evaluated for estimating the vehicle speed. One is using feature matching method between the two contiguous images; the other is via the pre-calculating distance matching of each pixel of the image. The testing speed range is from 0 to 110 km/hr and the processing frame rate is 5 fps. The accuracy of the speed estimation is the feature matching method: 8%; and the other method: 12%.

UR - http://www.scopus.com/inward/record.url?scp=84869854762&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84869854762&partnerID=8YFLogxK

U2 - 10.1109/UIC-ATC.2012.110

DO - 10.1109/UIC-ATC.2012.110

M3 - Paper

AN - SCOPUS:84869854762

SP - 832

EP - 834

ER -

Huang SC, Lin CC, Wang M-S. A vision-based vehicle speed warning system. 2012. Paper presented at 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012, Fukuoka, Japan. https://doi.org/10.1109/UIC-ATC.2012.110