Robust Object Tracking with Online Multiple Instance Learning

In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 33; no. 8; pp. 1619 - 1632
Main Authors:	Babenko, B., Ming-Hsuan Yang, Belongie, S.
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.08.2011 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation model Agriculture Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Boosting Classifiers Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Frames Learning Memory organisation. Data processing multiple instance learning On-line systems Online online boosting Pattern recognition. Digital image processing. Computational geometry Real time Software Studies Target tracking Theoretical computing Tracking Training Trains Visual Tracking Computer vision Discriminant analysis Data analysis Target tracking On line Tracking Motion estimation Image processing Video signal Visual Tracking Real time Aggregate model Bootstrapping Supervised learning Remolded sample Classification multiple instance learning online boosting Bootstrap Localization
ISSN:	0162-8828, 1939-3539, 1939-3539
Online Access:	Get full text
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