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MULTIZOOM ACTIVITY RECOGNITION USING MACHINE LEARNING
| Title: | MULTIZOOM ACTIVITY RECOGNITION USING MACHINE LEARNING. |
 32 views 14 downloads |
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| Name(s): |
Smith, Raymond, Author Shah, Mubarak, Committee Chair University of Central Florida, Degree Grantor |
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| 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. |
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| Subject(s): |
Activity Recognition Action Recognition Machine Learning Adaboost TemporalBoost |
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| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0000865 | |
| Restrictions on Access: | campus 2015-01-31 | |
| Host Institution: | UCF |
