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Autonomous target detection using segmented correlation method and tracking via mean shift algorithm

Munawar, A. and Kamal, Khurram and Qaisar, A. and Ejaz, A. (2011) Autonomous target detection using segmented correlation method and tracking via mean shift algorithm. In: 2011 4th International Conference on Mechatronics: Integrated Engineering for Industrial and Societal Development, ICOM'11 - Conference Proceedings. IEEE Explorer, pp. 1-6. ISBN 978-1-61284-435-0

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Official URL: http://dx.doi.org/10.1109/ICOM.2011.5937148

Abstract

An autonomous, efficient and effective object tracking algorithm was required to autonomously identify and track incoming targets. Then controlling a pan-tilt mounted with the sensing camera to accommodate the target within the camera's field of view and controlling a weapon mounted on the second mechanical pan tilt to lock the target and follow it efficiently and accurately. A hybrid algorithm is derived that is a combination of an intruder identification and localization technique derived from the normalized cross correlation method. Spatial and dimensional parameters of the target are autonomously retrieved from segmented correlation method, which are then used as the input parameters for the mean shift algorithm.

Item Type:Book Section
Uncontrolled Keywords:autonomous parameters detection using segmented correlation, hybrid algorithm, mean shift tracking
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:28919
Deposited By: Liza Porijo
Deposited On:04 Dec 2012 03:51
Last Modified:04 Dec 2012 03:51

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