Detection of Traffic and Road Condition Based on SVM and PHOW

Shi Jie Jia; Yu Ting Zhai; Xiao Wei Jia

doi:10.4028/www.scientific.net/AMM.513-517.3651

Paper Titles

Lifetime-Aware Capacity Dynamic Adjusting for Solid State Disk
p.3630

A Flexible Hemispherical Coverage Array Antenna Based on Multi-Planar Arrays
p.3634

Comparison of Shannon Capacity between OFDM and DFT-S OFDM
p.3639

Study on Traffic Control of Single Intersection Based on Rough Set
p.3644

Detection of Traffic and Road Condition Based on SVM and PHOW
p.3651

A Study on the Cognitive Process of Stereopsis in Small Uncrossed Disparity
p.3655

Application of Radar Velocity Tomography for Detecting Coal and Gas Outburst in Deep Coal Mining
p.3659

A Chemical Embedded Video Monitoring System Based on Real-Time Video Compression MPEG4 Encoding
p.3663

Design of Digital Broadcast Radio System Based on STM32
p.3667

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Detection of Traffic and Road Condition Based on...

Detection of Traffic and Road Condition Based on SVM and PHOW

Abstract:

To avoid the defects of physical detection methods, such as road spoiling, complex algorithms and equipment maintenance, SVM (Support Vector Machine) and PHOW (Pyramid Histogram of Words) are explored to detect the traffic and road condition. Experimental results show that the average accuracies go beyond 82.5% in the case of different weather. The proposed method can effectively classify 4 types of traffic and road condition images.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

3651-3654

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.3651

Citation:

Cite this paper

Online since:

February 2014

Authors:

Shi Jie Jia*, Yu Ting Zhai, Xiao Wei Jia

Keywords:

PHOW, Road Condition Detect, Support Vector Machine (SVM), Traffic Condition Detect

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Fan Xiaoke. Research on the Problem of Urban Traffic Congestion[J]. China Public Security: Academy Edition. 2007(1): pp.48-51.

Google Scholar

[2] Lazebnik S, Schmid C, and Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories[C]. Proceedings of the IEEE Computer Society Conference of Computer Vision and Pattern Recognition (CVPR'06), New York, USA, June 17-22, 2006, Vol 2: 2169-2178.

DOI: 10.1109/cvpr.2006.68

Google Scholar

[3] Bosch, A., X. Muñoz, and R. Martí, Which is the best way to organize/classify images by content[J] Image and vision computing, 2007. 25(6): pp.778-791.

DOI: 10.1016/j.imavis.2006.07.015

Google Scholar

[4] JiaShijie, KongXiangwei, FuHaiyan, etal. Auto Classification of Product Images Based on Complementary Features and Class Descriptor[J]. Journal of Electronics and Information Technology. 2010. 32(10): pp.2294-2300.

DOI: 10.3724/sp.j.1146.2009.01391

Google Scholar

[5] Cortes, C. and V. Vapnik, Support-vector networks[J]. Machine learning, 1995. 20(3): pp.273-297.

DOI: 10.1007/bf00994018

Google Scholar

[6] Wu, X., V. Kumar. The Top Ten Algorithms in Data Mining[M]. Boca Raton, FL : Chapman and Hall/CRC. Vol. 9. (2009).

Google Scholar

[7] Yong. H, Chun-ying. Z, Zhong-hu.S. Multi-class Support Vector Machines Algorithm Summarization [J]. Computing Technology and Automation. 2005, 24(4): pp.1003-6199.

Google Scholar

[8] Bosch A, Zisserman A, and Munoz X. Representing shape with a spatial pyramid kernel[C]. Proceedings of the 6th ACM international conference on Image and video retrieval, Amsterdam, Dutch, 2007: 401-408.

DOI: 10.1145/1282280.1282340

Google Scholar

[9] ZhaiYuting, JiaShijie. Traffic Conditions Detection Based on Digital Image and Support Vector Machine[J]. Computer Engineering and Design. (to be published).

Google Scholar

[10] He, K., J. Sun, and X. Tang, Single image haze removal using dark channel prior[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011. 33(12): pp.2341-2353.

DOI: 10.1109/tpami.2010.168

Google Scholar