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

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 seco...

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Main Author: K., Kamal
Format: Conference or Workshop Item
Published: 2011
Online Access:http://eprints.utm.my/id/eprint/45623/
http://dx.doi.org/10.1109/ICOM.2011.5937148
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id my.utm.45623
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spelling my.utm.456232017-09-04T04:58:51Z http://eprints.utm.my/id/eprint/45623/ Autonomous target detection using segmented correlation method and tracking via mean shift algorithm K., Kamal 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. 2011 Conference or Workshop Item PeerReviewed K., Kamal (2011) Autonomous target detection using segmented correlation method and tracking via mean shift algorithm. In: 4th International Conference on Mechatronics (ICOM), 17-19 May 2011, Kuala Lumpur, Malaysia. http://dx.doi.org/10.1109/ICOM.2011.5937148
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
description 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.
format Conference or Workshop Item
author K., Kamal
spellingShingle K., Kamal
Autonomous target detection using segmented correlation method and tracking via mean shift algorithm
author_facet K., Kamal
author_sort K., Kamal
title Autonomous target detection using segmented correlation method and tracking via mean shift algorithm
title_short Autonomous target detection using segmented correlation method and tracking via mean shift algorithm
title_full Autonomous target detection using segmented correlation method and tracking via mean shift algorithm
title_fullStr Autonomous target detection using segmented correlation method and tracking via mean shift algorithm
title_full_unstemmed Autonomous target detection using segmented correlation method and tracking via mean shift algorithm
title_sort autonomous target detection using segmented correlation method and tracking via mean shift algorithm
publishDate 2011
url http://eprints.utm.my/id/eprint/45623/
http://dx.doi.org/10.1109/ICOM.2011.5937148
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score 13.160551

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