{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/46366"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/46366","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A dynamic key frames approach to object tracking","abstract":"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.","abstract_html":"In this thesis, I present a dynamic key frames algorithm for state estimation from observations. 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