The Application of Gaussian Mixture Modeling in Traffic Flow Videos

Wen Yan Lv; Hong Ye Yang; Rui Wang; Jin Ying Li

doi:10.4028/www.scientific.net/AMM.721.762

Paper Titles

Power Control Optimization for Position Tracking in Vehicle Network
p.740

An Optimized Design of Safety Messages’ Communication Sub-Layer in Vehicle Ad-Hoc Network
p.744

RETRACTED: Visualizing Interrupts and Replication with Timer
p.750

An Effective Program Clustering Algorithm for TV Recommendation System
p.757

The Application of Gaussian Mixture Modeling in Traffic Flow Videos
p.762

Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition
p.766

Design and Algorithm Optimization of P2P Mobile Monitoring Network-Based Facial Recognition System
p.771

Rotation Moment Invariant Feature Extraction Techniques for Image Matching
p.775

The Application of Improved PSO Algorithm in PMMW Image OSTU Threshold Segmentation
p.779

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 721The Application of Gaussian Mixture Modeling in...

The Application of Gaussian Mixture Modeling in Traffic Flow Videos

Abstract:

Traffic flow video processing is an important direction of the development of intelligent transportation, Gaussian mixture background modeling is one of commonly algorithms to build background modeling that is widely used in image processing field. In order to analysis the algorithm comprehensively ,in this paper, it calculus on formula as well as tests and compares some traffic flow videos, the solution based on the K-means, EM parameters model are presented, it gets the mathematical meaning and evolution process of the algorithm clearly by calculating formulas to provide theoretical guidance for practical application; the traffic flow videos processing can get the treatment ability of this algorithm according to the different situation of videos, analyze the mixed Gauss background modeling for time migration, the light changing, moving object shadow processing ability. In this paper, the algorithm of mixed Gauss background modeling is analyzed from two aspects of theory and application.

You might also be interested in these eBooks

Vehicle, Mechanical and Electrical Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 721)

Pages:

762-765

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.721.762

Citation:

Cite this paper

Online since:

December 2014

Authors:

Wen Yan Lv*, Hong Ye Yang, Rui Wang, Jin Ying Li

Keywords:

Gauss Mixed Background Modeling, Image Processing, Intelligent Transportation, Traffic Flow

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Tan Wenming. Modeling method of complex background and its application in detection of moving targets [D]. University of Science & Technology China, Beijing (2009).

Google Scholar

[2] Stauffer C, WEL Crimson. Adaptive background mixture models for real-time tracking [C]. Proc of the IEEE Conference on Computer Vision and Pattern Recognition, 1999, 2: pp.246-252.

DOI: 10.1109/cvpr.1999.784637

Google Scholar

[3] Qu Zhaowei. The study on mixed traffic video detection algorithm. Changchun, Jilin University, (2009).

Google Scholar

[4] Zhu Qidan, Li Ke, Zhang Zhi, Cai Chengtao. Gauss hybrid adaptive background model improved Harbin; Journal of Harbin Engineering University: Vol31. No. 10. Oct 2010: pp.1348-1354.

Google Scholar

[5] Tian Hui. Research on traffic flow statistic algorithm based on video. Dalian; Dalian Marit ime University, (2012).

Google Scholar