Rao–Blackwellised particle filter for colour-based tracking

Colour-based particle filters have been used exhaustively in the literature, given rise to multiple applications. However, tracking coloured objects through time has an important drawback, since the way in which the camera perceives the colour of the object can change. Simple updates are often used to address this problem, which imply a risk of distorting the model and losing the target. In this paper, a joint image characteristic-space tracking is proposed, which updates the model simultaneously to the object location. In order to avoid the curse of dimensionality, a Rao–Blackwellised particle filter has been used. Using this technique, the hypotheses are evaluated depending on the difference between the model and the current target appearance during the updating stage. Convincing results have been obtained in sequences under both sudden and gradual illumination condition changes.

Research highlights
► Integrated framework to track simultaneously position and appearance parameters.
► Rao–Blackwellised particle filter + PDA Kalman filter for colour update.
► Algorithm robust against partial occlusions and sudden appearance changes.
► General approach that allows tracking parametric or non-parametric appearance models.