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.