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MULTIZOOM ACTIVITY RECOGNITION USING MACHINE LEARNING

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Date Issued:
2005
Abstract/Description:
In this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The approach taken here is to build a large set of activity features, though TemporalBoost itself is able to work with any feature set other boosting algorithms use. We also show how multiple levels of zoom can cooperate to solve problems related to activity recognition.
Title: MULTIZOOM ACTIVITY RECOGNITION USING MACHINE LEARNING.
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Name(s): Smith, Raymond, Author
Shah, Mubarak, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2005
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The approach taken here is to build a large set of activity features, though TemporalBoost itself is able to work with any feature set other boosting algorithms use. We also show how multiple levels of zoom can cooperate to solve problems related to activity recognition.
Identifier: CFE0000865 (IID), ucf:46658 (fedora)
Note(s): 2005-12-01
Ph.D.
Engineering and Computer Science, School of Computer Science
Doctorate
This record was generated from author submitted information.
Subject(s): Activity Recognition
Action Recognition
Machine Learning
Adaboost
TemporalBoost
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000865
Restrictions on Access: campus 2015-01-31
Host Institution: UCF

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