Skip navigation
Skip navigation

Boosting adaptive linear weak classifiers for online learning and tracking

Parag, Toufiq; Porikli, Fatih; Elgammal, Ahmed

Description

Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak classifiers/features to adapt with the change over time. We are proposing a new online boosting algorithm where the form of the weak classifiers themselves are modified to cope with scene changes. Instead of replacement, the parameters of the weak classifiers are altered in accordance with the new data subset presented...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
URI: http://hdl.handle.net/1885/32971
Source: Proceedings of CVPR 2008
DOI: 10.1109/CVPR.2008.4587556

Download

File Description SizeFormat Image
01_Parag_Boosting_adaptive_linear_weak_2008.pdf1.99 MBAdobe PDF    Request a copy


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  27 November 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator