A Region-based Object Tracking Method Using AdaBoost-based Feature Selection

碩士 === 國立中正大學 === 資訊工程所 === 94 === We proposed an integrated tracking system for applications which need more precise segmentation of target. The main tracking mechanism is accomplished by two trackers. The first tracker performs tracking by Adaboost on pixel-based seed features; it can provide more...

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Main Authors: Fan-tung Wei, 魏藩東
Other Authors: Chia-wen Lin
Format: Others
Language:en_US
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/91912251890535846637
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spelling ndltd-TW-094CCU053920812015-10-13T11:31:38Z http://ndltd.ncl.edu.tw/handle/91912251890535846637 A Region-based Object Tracking Method Using AdaBoost-based Feature Selection 以區塊為基礎之物件追蹤技術結合以Adaboost為基礎之特徵選擇 Fan-tung Wei 魏藩東 碩士 國立中正大學 資訊工程所 94 We proposed an integrated tracking system for applications which need more precise segmentation of target. The main tracking mechanism is accomplished by two trackers. The first tracker performs tracking by Adaboost on pixel-based seed features; it can provide more detailed segmentation of target. The second tracker achieves tracking by bidirectional k-means clustering on regions, and uses Adaboost on region-based seed features to provide compensations to the short the first tracker. We also implement a tool which allows users to refine the result manually. By confidence measure, users can easily choose the timing to interact with frames which probably are in occlusion or perform poorly due to some complex situation. Chia-wen Lin 林嘉文 2007 學位論文 ; thesis 55 en_US
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description 碩士 === 國立中正大學 === 資訊工程所 === 94 === We proposed an integrated tracking system for applications which need more precise segmentation of target. The main tracking mechanism is accomplished by two trackers. The first tracker performs tracking by Adaboost on pixel-based seed features; it can provide more detailed segmentation of target. The second tracker achieves tracking by bidirectional k-means clustering on regions, and uses Adaboost on region-based seed features to provide compensations to the short the first tracker. We also implement a tool which allows users to refine the result manually. By confidence measure, users can easily choose the timing to interact with frames which probably are in occlusion or perform poorly due to some complex situation.
author2 Chia-wen Lin
author_facet Chia-wen Lin
Fan-tung Wei
魏藩東
author Fan-tung Wei
魏藩東
spellingShingle Fan-tung Wei
魏藩東
A Region-based Object Tracking Method Using AdaBoost-based Feature Selection
author_sort Fan-tung Wei
title A Region-based Object Tracking Method Using AdaBoost-based Feature Selection
title_short A Region-based Object Tracking Method Using AdaBoost-based Feature Selection
title_full A Region-based Object Tracking Method Using AdaBoost-based Feature Selection
title_fullStr A Region-based Object Tracking Method Using AdaBoost-based Feature Selection
title_full_unstemmed A Region-based Object Tracking Method Using AdaBoost-based Feature Selection
title_sort region-based object tracking method using adaboost-based feature selection
publishDate 2007
url http://ndltd.ncl.edu.tw/handle/91912251890535846637
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