Document Type

Dissertation

Date of Degree

Fall 2011

Degree Name

PhD (Doctor of Philosophy)

Degree In

Computer Science

First Advisor

David Eichmann

Abstract

Detection of events and actions in video entails substantial processing of very large, even open-ended, video streams. Video data presents a unique challenge for the information retrieval community because properly representing video events is challenging. We propose a novel approach to analyze temporal aspects of video data. We consider video data as a sequence of images that form a 3-dimensional spatiotemporal structure, and perform multiview orthographic projection to transform the video data into 2-dimensional representations. The projected views allow a unique way to rep- resent video events and capture the temporal aspect of video data. We extract local salient points from 2D projection views and perform detection-via-similarity approach on a wide range of events against real-world surveillance data. We demonstrate our example-based detection framework is competitive and robust. We also investigate the synthetic example driven retrieval as a basis for query-by-example.

Keywords

Motion analysis, Video event detection, Video retrieval

Pages

ix, 115 pages

Bibliography

Includes bibliographical references (pages 103-115).

Copyright

Copyright 2011 Dong-Jun Park

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