Parametric contour tracking using unscented Kalman filter

This paper presents an efficient method to integrate various spatial-temporal constraints to regularize the contour tracking. Specifically, the global shape prior, contour smoothness and object dynamics are addressed. First, the contour is represented as a parametric shape, based on which a causal smoothness constraint can be developed to exploit the local spatial constraint. The causality nature of the constraint allows us to do efficient probabilistic contour detection using the powerful Hidden Markov Model (HMM). Finally, a unscented Kalman filter (UKF) is applied to estimate object parameters based on the non-linear observation model (i.e. the relationship between the detected contour points and the contour parameters) and the object dynamics. Better than other variants of the recursive least mean square estimators (e.g., extended Kalman filter), UKF approximates non-linear systems up to the second order (third for Gaussian prior) with similar computational cost. This novel tracking algorithm is running in real-time and robust to severe distractions due to the comprehensive spatial-temporal constraints. It is applied to track people in bad illumination and cluttered environments. Promising results are reported.