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)

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