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NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO

Liu, Qiang (2005) NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

The goal of this thesis is to find a highly efficient algorithm to compress patient 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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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Liu, Qiangqliu@neuronet.pitt.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSclabassi, Robert J.bobs@neuronet.pitt.edu
Committee CoChairSun, Minguimrsun@neuronet.pitt.eduDRSUN
Committee MemberLi, Ching-Chungccl@engr.pitt.eduCCL
Committee MemberBoston, J. Robertboston@engr.pitt.eduBBN
Committee MemberYang, Jiejie.yang@cs.cmu.edu
Committee MemberChaparro, Luis F.chaparro@ee.pitt.eduLFCH
Date: 21 June 2005
Date Type: Completion
Defense Date: 8 April 2005
Approval Date: 21 June 2005
Submission Date: 8 March 2005
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Change detection; Motion detection; MPEG-4; Object-based encoding; Patient monitoring; Video coding; Video object; Video segmentation; Video surveilance
Other ID: http://etd.library.pitt.edu/ETD/available/etd-03082005-145611/, etd-03082005-145611
Date Deposited: 10 Nov 2011 19:32
Last Modified: 15 Nov 2016 13:36
URI: http://d-scholarship.pitt.edu/id/eprint/6455

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