Keywords
Activity Recognition, Action Recognition, Machine Learning, Adaboost, TemporalBoost
Abstract
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.
Notes
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Graduation Date
2005
Semester
Fall
Advisor
Shah, Mubarak
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Format
application/pdf
Identifier
CFE0000865
URL
http://purl.fcla.edu/fcla/etd/CFE0000865
Language
English
Release Date
January 2015
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Smith, Raymond, "Multizoom Activity Recognition Using Machine Learning" (2005). Electronic Theses and Dissertations. 4461.
https://stars.library.ucf.edu/etd/4461