2026-09-18T23:36:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/463662022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David Demirdjian.
Wilkens, Christopher A
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2009-08-25T17:59:38Z
2009-08-25T17:59:38Z
2008
2008
http://hdl.handle.net/1721.1/46366
417813636
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 95-96).
In this thesis, I present a dynamic key frames algorithm for state estimation from observations. The algorithm uses KL-divergence as a metric to identify the frames that contribute the most information to estimation of the system's current state. The algorithm is first presented in a numerical optimization framework and then developed as an extension to the Condensation algorithm. Finally, I present results from a Matlab simulation of the algorithm.
by Christopher A. Wilkens.
M.Eng.
96 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A dynamic key frames approach to object tracking
Thesis
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