Geodesic
Automation of the traffic flow data collection and analysis process to avoid traffic congestions in Mogilev
Problemy fiziki, matematiki i tehniki, no. 2 (2014), pp. 84-88.

Voir la notice de l'article provenant de la source Math-Net.Ru

The analysis of commonly used traffic detectors and advantages and disadvantages of optical traffic detectors (IP-cameras)is given. The model of the traffic flow data collection and analysis automation system and the method of modification of the Multi-Layer Background Subtraction algorithm using parallel computing based on the graphic processors are suggested. The experimental investigations show that the modified method can help to decrease video frame processing speed in 1.9 times.
Keywords: traffic flow, traffic detector, traffic density, traffic flow intensity, Multi-Layer Background Subtraction algorithm. 
@article{PFMT_2014_2_a13,
     author = {I. A. Melnikau and K. A. Demedenkov and I. A. Yeuseyenka},
     title = {Automation of the traffic flow data collection and analysis process to avoid traffic congestions in {Mogilev}},
     journal = {Problemy fiziki, matematiki i tehniki},
     pages = {84--88},
     publisher = {mathdoc},
     number = {2},
     year = {2014},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/PFMT_2014_2_a13/}
}
TY  - JOUR
AU  - I. A. Melnikau
AU  - K. A. Demedenkov
AU  - I. A. Yeuseyenka
TI  - Automation of the traffic flow data collection and analysis process to avoid traffic congestions in Mogilev
JO  - Problemy fiziki, matematiki i tehniki
PY  - 2014
SP  - 84
EP  - 88
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/PFMT_2014_2_a13/
LA  - ru
ID  - PFMT_2014_2_a13
ER  - 
%0 Journal Article
%A I. A. Melnikau
%A K. A. Demedenkov
%A I. A. Yeuseyenka
%T Automation of the traffic flow data collection and analysis process to avoid traffic congestions in Mogilev
%J Problemy fiziki, matematiki i tehniki
%D 2014
%P 84-88
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/PFMT_2014_2_a13/
%G ru
%F PFMT_2014_2_a13
I. A. Melnikau; K. A. Demedenkov; I. A. Yeuseyenka. Automation of the traffic flow data collection and analysis process to avoid traffic congestions in Mogilev. Problemy fiziki, matematiki i tehniki, no. 2 (2014), pp. 84-88. http://geodesic.mathdoc.fr/item/PFMT_2014_2_a13/

[1] Sostoyanie okruzhayuschei sredy Respubliki Belarus, nats. doklad, M-vo prirod. resur. i okruzhayuschei sredy Respubliki Belarus, Gos. nauch. uchr-e «Ins-t prirodopolzovaniya Nats. akadem. nauk Belarusi», Beltamozhservis, Minsk, 2010, 150 pp.

[2] Istoriya Gosavtoinspektsii Mogilevskoi oblasti, , Upravlenie Gosavtoinspektsii UVD Mogilevskogo Oblispolkoma (Data dostupa: 01.11.2013) http://gai.mogilev.by/gai/history.htm

[3] Analiticheskii obzor detektorov transporta, , AGA Group Inc. (Data dostupa: 01.11.2013) http://www.againc.net/ru/education/transport-engineering/10-detectors

[4] A. A. Lukyanitsa, A. G. Shishkin, Tsifrovaya obrabotka videoizobrazhenii, «Ai-Es-Es Press», M., 2009, 518 pp.

[5] Z. Zivkovic, “Improved Adaptive Gaussian Mixture Model for Background Subtraction”, Proceedings of the 17th International Conference on Pattern Recognition, v. 2, 2004, 28–31 | DOI

[6] Yao Jian, Jean-Marc Odobez, “Multi-Layer Background Subtraction Based on Color and Texture”, Proceedings of the Conference on Computer Vision and Pattern Recognition (Minneapolis, 2007), 1–8

[7] N. M. Oliver, B. Rosario, A. P. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (2000), 831–843 | DOI