Particle filters are often used for tracking objects within a scene. As the prediction model of a particle filter is often implemented using basic movement predictions such as ran- domwalk,constantvelocityoracceleration,thesemodelswill usually be incorrect. Therefore, this paper proposes a new approach, based on a Canonical Correlation Analysis (CCA) tracking method which provides an object specific motion model. This model is used to construct a proposal distribu- tion of the prediction model which predicts new states, in- creasing the robustness of the particle filter. Results confirm anincreaseinaccuracycomparedtostate-of-the-artmethods.

paper_VCIP12_25_final_final.pdf

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