UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Subject: search.filters.subject.Kullback-Leibler distance

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    HUMAN APPEARANCE MODELING IN VISUAL SURVEILLANCE
    (2007-08-07) Yu, Yang; Chellapa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    We present an appearance model for establishing correspondence between tracks of people which may be taken at different places, at different times or across different cameras. Illumination insensitive color features, i.e., RGB rank feature and brightness-color feature are used. Path-length feature is added for structural information and invariance to motion and pose. The appearance model is constructed by kernel density estimation. Kullback-Leibler distance measures the similarity between the models. To further exploit the information in video sequence, key frame selection method and online hierarchical clustering algorithm are proposed to construct appearance model from video. Key frame selection use the frames with large information gain to represent the appearance model. Online hierarchical clustering algorithm condense the model into a few clusters in the framework of our appearance model. Experimental results demonstrate the important role of the path-length feature in the appearance model and the effectiveness of the proposed appearance model and matching method.