The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme

碩士 === 中華大學 === 資訊工程學系碩士班 === 103 === Object detection and tracking approaches are crucial to the video surveillance and ecological investigation. In these applications, PTZ cameras are often applied to extract the close-up views by controlling the pan, tilt, and zoom operations in a large area. Hen...

Full description

Bibliographic Details
Main Authors: Tai, Chun-Feng, 戴群鋒
Other Authors: Lien, Cheng-Chang
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/18156867277415095465
id ndltd-TW-103CHPI5392021
record_format oai_dc
spelling ndltd-TW-103CHPI53920212017-02-19T04:30:57Z http://ndltd.ncl.edu.tw/handle/18156867277415095465 The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme 結合KLT特徵點預測機制提升SURF特徵點在動態環境中之追蹤連續性 Tai, Chun-Feng 戴群鋒 碩士 中華大學 資訊工程學系碩士班 103 Object detection and tracking approaches are crucial to the video surveillance and ecological investigation. In these applications, PTZ cameras are often applied to extract the close-up views by controlling the pan, tilt, and zoom operations in a large area. Hence, this study aims at the developing of a robust and automatic object detection and tracking with single PTZ camera. Conventional object detection and tracking with single PTZ camera often apply feature-based traditional target tracking methods. However, these method can fail under the dynamic outdoor environments with serious variations of background and illuminations. In this study, we try to integrate the prediction scheme of KLT algorithm and SURF-based target tracking scheme to develop a robust object tracking with single PTZ camera under the dynamic outdoor environments. The experimental results show that the proposed method outperform the conventional methods. Lien, Cheng-Chang 連振昌 2015 學位論文 ; thesis 34 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 中華大學 === 資訊工程學系碩士班 === 103 === Object detection and tracking approaches are crucial to the video surveillance and ecological investigation. In these applications, PTZ cameras are often applied to extract the close-up views by controlling the pan, tilt, and zoom operations in a large area. Hence, this study aims at the developing of a robust and automatic object detection and tracking with single PTZ camera. Conventional object detection and tracking with single PTZ camera often apply feature-based traditional target tracking methods. However, these method can fail under the dynamic outdoor environments with serious variations of background and illuminations. In this study, we try to integrate the prediction scheme of KLT algorithm and SURF-based target tracking scheme to develop a robust object tracking with single PTZ camera under the dynamic outdoor environments. The experimental results show that the proposed method outperform the conventional methods.
author2 Lien, Cheng-Chang
author_facet Lien, Cheng-Chang
Tai, Chun-Feng
戴群鋒
author Tai, Chun-Feng
戴群鋒
spellingShingle Tai, Chun-Feng
戴群鋒
The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme
author_sort Tai, Chun-Feng
title The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme
title_short The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme
title_full The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme
title_fullStr The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme
title_full_unstemmed The Improvement of SURF Point Tracking Continuity under the Dynamic Environments by using KLT Prediction Scheme
title_sort improvement of surf point tracking continuity under the dynamic environments by using klt prediction scheme
publishDate 2015
url http://ndltd.ncl.edu.tw/handle/18156867277415095465
work_keys_str_mv AT taichunfeng theimprovementofsurfpointtrackingcontinuityunderthedynamicenvironmentsbyusingkltpredictionscheme
AT dàiqúnfēng theimprovementofsurfpointtrackingcontinuityunderthedynamicenvironmentsbyusingkltpredictionscheme
AT taichunfeng jiéhéklttèzhēngdiǎnyùcèjīzhìtíshēngsurftèzhēngdiǎnzàidòngtàihuánjìngzhōngzhīzhuīzōngliánxùxìng
AT dàiqúnfēng jiéhéklttèzhēngdiǎnyùcèjīzhìtíshēngsurftèzhēngdiǎnzàidòngtàihuánjìngzhōngzhīzhuīzōngliánxùxìng
AT taichunfeng improvementofsurfpointtrackingcontinuityunderthedynamicenvironmentsbyusingkltpredictionscheme
AT dàiqúnfēng improvementofsurfpointtrackingcontinuityunderthedynamicenvironmentsbyusingkltpredictionscheme
_version_ 1718415528985362432