Linear Regression and Adaptive Appearance Models for Fast Simultaneous Modelling and Tracking

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00182044" target="_blank" >RIV/68407700:21230/11:00182044 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s11263-010-0364-4" target="_blank" >http://dx.doi.org/10.1007/s11263-010-0364-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11263-010-0364-4" target="_blank" >10.1007/s11263-010-0364-4</a>

Result language

angličtina

Original language name

Linear Regression and Adaptive Appearance Models for Fast Simultaneous Modelling and Tracking

Original language description

We proposes an approach to tracking by regression that uses no hard-coded models and no offline learning stage. The Linear Predictor (LP) tracker has been shown to be highly computationally efficient, resulting in fast tracking. Regression tracking techniques tend to require offline learning to learn suitable regression functions. We removes offline learning and therefore increases the applicability of the technique. The online-LP tracker can simply be seeded with an initial target location, akin to theubiquitous Lucas-Kanade algorithm that tracks by registering an image template via minimisation. The issue is the representation of the target appearance and how this representation is able to adapt to changes in target appearance over time. We proposedtwo methods, LP-SMAT and LP-MED, demonstratthe ability to adapt to large appearance variations by incrementally building an appearance model.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/GA102%2F07%2F1317" target="_blank" >GA102/07/1317: Methods for Visual Recognition of Large Collections of Non-rigid Objects</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2011

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Computer Vision

ISSN

0920-5691

e-ISSN

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Volume of the periodical

95

Issue of the periodical within the volume

2

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

26

Pages from-to

154-179

UT code for WoS article

000294566000004

EID of the result in the Scopus database

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