A Feature Extraction Approach of Traffic Congestion from Video

Yuan Feng Yang; Xue Feng Xian; Li Li Liao; Min Ya Zhao

doi:10.4028/www.scientific.net/AMR.490-495.1058

Paper Titles

H_∞ Control for Networked Systems with Random Time Delay and Data Packet Dropout
p.1039

MEMS Sensor and Electrostatic Detection Technology in the Application of New Type Composite Fuze
p.1044

Experimental Investigation of Rotor Rubbing Faults
p.1048

Modeling and Analyzing the Voltage-Displacement Performance of Cymbal Piezoelectric Transducers
p.1053

A Feature Extraction Approach of Traffic Congestion from Video
p.1058

Implementation of an Intelligent Integrated Detection System Based on Virtual Instrument for Different Missiles
p.1063

MFL Detection Defect Reconstruction of Pipeline Based on Conjugate Gradient Methods
p.1066

Feature Extraction of Gearbox Compound Faults Based on Blind Source Separation
p.1071

Lifetime Computational Models of Rolling Bearings in a 1.5MW Wind Turbine’s Epicyclic Gearbox
p.1076

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495A Feature Extraction Approach of Traffic...

A Feature Extraction Approach of Traffic Congestion from Video

Abstract:

To avoid the difficulty of collecting accurate traffic flow data, this paper proposes a novel approach for congestion features extraction from traffic video. The approach firstly segments the traffic video into shots and the shot motion content feature is extracted. Then, we extract the key frames applying an improved global k-means clustering algorithm. The last congestion feature of the global optical flow energy is computed based on the key frames. The numerical experiments on traffic surveillance video show the validity and high accuracy for traffic congestion detection using the propose method in this paper

You might also be interested in these eBooks

Mechatronics and Intelligent Materials II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

1058-1062

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.1058

Citation:

Cite this paper

Online since:

March 2012

Authors:

Yuan Feng Yang, Xue Feng Xian, Li Li Liao, Min Ya Zhao

Keywords:

Feature Extraction, Global Optical Flow, Motion Content, Traffic Congestion

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] T. Thianniwet, S. Phosaard and W. Pattara-Atikom, Classification of road traffic congestion levels from vehicle's moving patterns: A comparison between artificial neural network and decision tree algorithm, Lecture Notes in Electrical Engineering, Vol. 60 LNEE, pp.261-271, (2010).

DOI: 10.1007/978-90-481-8776-8_23

Google Scholar

[2] S. Sananmongkhonchai, P. Tangamchit and P. Pongpaibool, Road traffic estimation from multiple GPS data using incremental weighted update, Proceedings - 8th International Conference on Intelligent Transport System Telecommunications, pp.62-66, (2008).

DOI: 10.1109/itst.2008.4740228

Google Scholar

[3] W. Pattara-atikom, P. Pongpaibool and S. Thajchayapong, Estimating road traffic congestion using vehicle velocity, proc. 6th International Conference on ITS Telecommunications, pp.1001-1004, Jun, (2006).

DOI: 10.1109/itst.2006.288722

Google Scholar

[4] JungHwan Oh and Babitha Bandi, Multimedia data mining framework for raw video sequences, In Proc. of ACM Third International Workshop on Multimedia Data Mining (MDM/KDD2002), pp.18-35, July (2002).

DOI: 10.1007/978-3-540-39666-6_2

Google Scholar

[5] HE Chun-Lin, The New Vehicle Detection Algorithm Based on Video, Computer Science, vol. 32, no. 5, pp.243-245, (2005).

Google Scholar

[6] B. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proc. Seventh International Joint Conference on Artificial Intelligence, Vancouver, Canada, p.674–679, (1981).

Google Scholar

[7] C. Cortes and V. Vapnik, Support-vector network, Machine learning, Vol. 20, Number 3, pp.273-297, Sep. (1995).

Google Scholar