Browsing by Author Dai, Xiangtian
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This thesis presents a visual tracking framework, Component-based Tracking. This framework combines low-level visual clues and constraints in a systematic way. It is based on a probabilistic graphical model to infer the marginal probability density function of the state of a tracked target. In this framework, all potential functions are approximated as weighted sums of multivariate Gaussians so probabilistic inference by message passing is equivalent to hypotheses combination and evaluation. The possibility of low level detectors not providing useful information is taken into account by the introduction of a null hypothesis. Simulation is conducted to verify the framework. In additio...