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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 109 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  2/2010 - 5

Kalman Filter Based Tracking in an Video Surveillance System

SULIMAN, C. See more information about SULIMAN, C. on SCOPUS See more information about SULIMAN, C. on IEEExplore See more information about SULIMAN, C. on Web of Science, CRUCERU, C. See more information about  CRUCERU, C. on SCOPUS See more information about  CRUCERU, C. on SCOPUS See more information about CRUCERU, C. on Web of Science, MOLDOVEANU, F. See more information about MOLDOVEANU, F. on SCOPUS See more information about MOLDOVEANU, F. on SCOPUS See more information about MOLDOVEANU, F. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (2,017 KB) | Citation | Downloads: 1,590 | Views: 4,947

Author keywords
video surveillance system, optical flow, Kalman filtering, image processing, tracking

References keywords
tracking(11), surveillance(8), video(6), intern(5), human(5), processing(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-05-31
Volume 10, Issue 2, Year 2010, On page(s): 30 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.02005
Web of Science Accession Number: 000280312600005
SCOPUS ID: 77954639594

Abstract
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In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a contact in a surveillance video, we have used the Horn-Schunk optical flow algorithm. The position and the behavior of the correctly detected contact were monitored with the help of the traditional Kalman filter. After that we have compared the results obtained from the optical flow method with the ones obtained from the Kalman filter, and we show the correct functionality of the Kalman filter based tracking. The tests were performed using video data taken with the help of a fix camera. The tested algorithm has shown promising results.


References | Cited By  «-- Click to see who has cited this paper

[1] M. A. Ali, S. Indupalli, B. Boufama, "Tracking Multiple People for Video Surveillance," First Intern. Workshop on Video Processing for Security, June 2006.

[2] B. Benfold, I. Reid, "Guiding Visual Surveillance by Tracking Human Attention," Proc. of the 20th British Machine Vision Conf., September 2009.

[3] L.M. Fuentes, S.A. Velastin, "From tracking to advanced surveillance," Proc. of the Intern. Conf. on Image Processing, vol. 3, pp. 121-124, September 2003.
[CrossRef]


[4] K.P. Horn, B.G. Schunck, "Determining optical flow," Artificial intelligence, vol. 17, pp. 185-203, 1981.
[CrossRef] [Web of Science Times Cited 4242] [SCOPUS Times Cited 3002]


[5] C. C. Hsieh, S. S. Hsu, "A Simple and Fast Surveillance System for Human Tracking and Behavior Analysis," Proc. of the 3rd Intern. IEEE Conf. on Signal-Image Technologies and Internet-Based System, pp. 812-818, December 2007.
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 8]


[6] F. Jean, R. Bergevin, A.B. Albu, "Body tracking in human walk from monocular video sequences," Proc. of the 2nd Canadian Conf. on Computer and Robot Vision, pp. 144-151, May 2005.

[7] N. Koenig, "Toward real-time human detection and tracking in diverse environments," Proc. of the 6th IEEE Intern. Conf. on Development and Learning, pp. 94-98, July 2007.
[CrossRef] [SCOPUS Times Cited 13]


[8] S. Kong, M.K. Bhuyan, C. Sanderson, B.C. Lovell, "Tracking of Persons for Video Surveillance of Unattended Environments," Proc. of the 19th Intern. Conf. on Pattern Recognition, pp. 1-4, December 2008.
[CrossRef]


[9] W. Niu, L. Jiao, D. Han, Y. F. Wang, "Real-time multiperson tracking in video surveillance," Proc. of the 4th Pacific Rim Conf. on Multimedia, vol. 2, pp. 1144-1148, December 2003.

[10] A.W. Senior, G. Potamianos, S. Chu, Z. Zhang, and A. Hampapur, "A comparison of multicamera person-tracking algorithms," Proc. IEEE Int. Works. Visual Surveillance, May 2006.

[11] J. Wang, Y.g Yin, H. Man, "Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model," EURASIP Journal on Image and Video Processing, vol. 2008, Article ID 969456, 10 pages, 2008.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 14]


[12] G. Welch, G. Bishop, "An Introduction to the Kalman Filter," Technical Report: TR95-041, University of North Carolina, 2006.

[13] J. Yao, J.M. Odobez, "Multi-Camera 3d Person Tracking With Particle Filter In A Surveillance Environment, "Proc. of the 16th European Signal Processing Conf., August 2008.

References Weight

Web of Science® Citations for all references: 4,271 TCR
SCOPUS® Citations for all references: 3,037 TCR

Web of Science® Average Citations per reference: 329 ACR
SCOPUS® Average Citations per reference: 234 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-11-18 17:32 in 43 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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