Accurate Real-TIme Object Tracking with Linear Prediction Method

This paper presents an efficient technique for real-time tracking of a single moving object in terrestrial scenes using a stationary camera. The tracking algorithm is based on the linear prediction (LP) solved by the maximum entropy method (MEM). It attempts to predict the centroid of the moving obj...

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Main Authors: P. Y., Yeoh, Syed Abu Bakar, Syed Abdul Rahman
Format: Article
Language:English
Published: 2003
Subjects:
Online Access:http://eprints.utm.my/id/eprint/1892/2/syed2003_AccurateRealtimeObjectTracking.pdf
http://eprints.utm.my/id/eprint/1892/
http:/dx.doi.org/10.1109/ICIP.2003.1247401
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spelling my.utm.18922013-12-18T03:14:04Z http://eprints.utm.my/id/eprint/1892/ Accurate Real-TIme Object Tracking with Linear Prediction Method P. Y., Yeoh Syed Abu Bakar, Syed Abdul Rahman TK Electrical engineering. Electronics Nuclear engineering This paper presents an efficient technique for real-time tracking of a single moving object in terrestrial scenes using a stationary camera. The tracking algorithm is based on the linear prediction (LP) solved by the maximum entropy method (MEM). It attempts to predict the centroid of the moving object in the next frame, based on several past centroid measurements. Using a second order of the linear prediction method, the proposed algorithm is able to accurately track the moving object. It is shown analytically that the proposed recursive predictor-corrector tracking algorithm is able to yield high accuracy performance and is superior to that of the Kalman filter, for a possibly random movement of single moving object. 2003-09-14 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/1892/2/syed2003_AccurateRealtimeObjectTracking.pdf P. Y., Yeoh and Syed Abu Bakar, Syed Abdul Rahman (2003) Accurate Real-TIme Object Tracking with Linear Prediction Method. International Conference on Image Processing, 3 . III-941-4. ISSN 1522-4880 http:/dx.doi.org/10.1109/ICIP.2003.1247401 DOI:10.1109/ICIP.2003.1247401
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/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
P. Y., Yeoh
Syed Abu Bakar, Syed Abdul Rahman
Accurate Real-TIme Object Tracking with Linear Prediction Method
description This paper presents an efficient technique for real-time tracking of a single moving object in terrestrial scenes using a stationary camera. The tracking algorithm is based on the linear prediction (LP) solved by the maximum entropy method (MEM). It attempts to predict the centroid of the moving object in the next frame, based on several past centroid measurements. Using a second order of the linear prediction method, the proposed algorithm is able to accurately track the moving object. It is shown analytically that the proposed recursive predictor-corrector tracking algorithm is able to yield high accuracy performance and is superior to that of the Kalman filter, for a possibly random movement of single moving object.
format Article
author P. Y., Yeoh
Syed Abu Bakar, Syed Abdul Rahman
author_facet P. Y., Yeoh
Syed Abu Bakar, Syed Abdul Rahman
author_sort P. Y., Yeoh
title Accurate Real-TIme Object Tracking with Linear Prediction Method
title_short Accurate Real-TIme Object Tracking with Linear Prediction Method
title_full Accurate Real-TIme Object Tracking with Linear Prediction Method
title_fullStr Accurate Real-TIme Object Tracking with Linear Prediction Method
title_full_unstemmed Accurate Real-TIme Object Tracking with Linear Prediction Method
title_sort accurate real-time object tracking with linear prediction method
publishDate 2003
url http://eprints.utm.my/id/eprint/1892/2/syed2003_AccurateRealtimeObjectTracking.pdf
http://eprints.utm.my/id/eprint/1892/
http:/dx.doi.org/10.1109/ICIP.2003.1247401
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score 13.160551