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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.6, No.5, Apr. 2014

Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching

Full Text (PDF, 329KB), PP.83-89


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Author(s)

G. Mallikarjuna Rao, Ch.Satyanarayana

Index Terms

Kalman Filter, Approximate Median Filter, Target tracking, Dynamic Template Matching

Abstract

In this work, we dealt with the tracking of single object in a sequence of frames either from a live camera or a previously saved video. A moving object is detected frame-by-frame with high accuracy and efficiency using Median approximation technique. As soon as the object has been detected, the same is tracked by kalman filter estimation technique along with a more accurate Template Matching algorithm. The templates are dynamically generated for this purpose. This guarantees any change in object pose which does not be hindered from tracking procedure. The system is capable of handling entry and exit of an object. Such a tracking scheme is cost effective and it can be used as an automated video conferencing system and also has application as a surveillance tool. Several trials of the tracking show that the approach is correct and extremely fast, and it's a more robust performance throughout the experiments.

Cite This Paper

G. Mallikarjuna Rao, Ch.Satyanarayana,"Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.5, pp.83-89, 2014. DOI: 10.5815/ijisa.2014.05.09

Reference

[1]Karan Gupta and Anjali V. Kulkarni, Implementation of an Automated Single Camera Object Tracking System Using Frame Differencing and Dynamic Template Matching. SCSS (1) 2007: 245-250 

[2]Alsaqre F.E., Baomng Y., “Multiple Moving Objects Tracking For Video Surveillance Systems”,ICSP'O4 Proceedings, Volume 13 no.04, pp.1301-1305, 2004.

[3]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.

[4]Haritaoglu I., Harwood D. and Davis L.S., “W4: Real-Time Surveillance of people and their Activities”, Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, no. 8, pp. 809-830, 2000

[5]Young Min Kim,” Object Tracking in a Video Sequence”,2007

[6]Aree Ali Mohammed, Astrid Laubenheimer Yusra and Ahmed Salih, Efficient Face Tracking and Detection in Video: Based on Template Matching, ht tp://elrond.informatik.tufreiberg.de/papers/WorldComp2012/IPC2078.pdf

[7]P. Sanyal et al., International Journal of Computer Science and Mobile Computing Vol.1, December- 2012, pg. 1-5

[8]Sivabalakrishnan.M and Dr.D.Manjula,” An Efficient Foreground Detection Algorithm for Visual Surveillance System “IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.5, May 2009

[9]N. McFarlane and C. Schofield, “Segmentation and tracking of piglets in images”, Machine Vision and Applications 8(3), pp. 187-193, 1995. 

[10]G. Welch and G. Bishop. An Introduction to the Kalman Filter, Proceedings of SIGGRAPH 2001, pp 19-24.

[11]LIU Zhong-pu, Shu-guang Zhao and PAN Xiang-he, “A New Method of Motion Object Tracking Based on Kalman Prediction and Mean-shift Search ”Optoelectronic Technology, Vol.29, pp. 30-33,2009(in Chinese).

[12]WENG Shiuh-ku, KUO Chung-ming and TU Shu-kang, “Video Object Tracking Using Adaptive Kalman Filter,” Journal of Visual Communication and Image Representation(S1190-1208),Vol.17, pp. 1194-1197, 2006

[13]http://www.mathworks.in/products/image/.