Bootstrapping Sequential Monte Carlo Tracking

Sequential Monte Carlo (SMC) methods have in recent years been applied to handle some of the problems inherent to model-based tracking. In this paper we suggest to apply bootstrapping to reduce the required number of particles in SMC tracking. By bootstrapping is meant to track reliable low-level image features and use them to bootstrap the high-level model-based tracking. The concept of bootstrapped SMC tracking is exemplified by monocular tracking of the 3D pose of a human arm with the position of the hand in the image as the bootstrapping information. Tests suggest that both bootstrapping is a sound strategies and an improvement over standard SMC-methods.

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