Type of Document Dissertation Author Liu, Qiang Author's Email Address qliu@neuronet.pitt.edu URN etd-03082005-145611 Title NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO Degree Doctor of Philosophy Program Electrical Engineering School School of Engineering Advisory Committee
Advisor Name Title Robert J. Sclabassi Committee Chair Mingui Sun Committee Co-Chair Ching-Chung Li Committee Member J. Robert Boston Committee Member Jie Yang Committee Member Luis F. Chaparro Committee Member Keywords
- MPEG-4
- Motion detection
- Change detection
- Object-based encoding
- Video object
- Video surveilance
- Video segmentation
- Video coding
- Patient monitoring
Date of Defense 2005-04-08 Availability unrestricted Abstract The goal of this thesis is to find a highly efficient algorithm to compresspatient monitoring video. This type of video mainly contains local motions
and a large percentage of idle periods. To specifically utilize these
features, we present an object-based approach, which decomposes input video
into three objects representing background, slow-motion foreground and
fast-motion foreground. Encoding these three video objects with different
temporal scalabilities significantly improves the coding efficiency in terms
of bitrate vs. visual quality.
The video decomposition is built upon change detection which identifies
content changes between video frames. To improve the robustness of capturing
small changes, we contribute two new change detection models. The model
built upon Markov random theory discriminates foreground containing the
patient being monitored. The other model, called covariance test method,
identifies constantly changing content by exploiting temporal correlation in
multiple video frames. Both models show great effectiveness in constructing
the defined video objects. We present detailed algorithms of video object
construction, as well as experimental results on the object-based coding of
patient monitoring video.
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