Comparison of Statistical and Shape-Based Approaches for Non-rigid Motion Tracking with Missing Data Using a Particle Filter

Recent developments in dynamic contour tracking in video sequences are based on prediction using dynamical models. The parameters of these models are fixed by learning the dynamics from a training set to represent plausible motions, such as constant velocity or critically damped oscillations. Thus, a problem arise in cases of non-constant velocity and unknown interframe motion, i.e. unlearned motions, and the CONDENSATION algorithm fails to track the dynamic contour. The main contribution of this work is to propose an adaptative dynamical model which parameters are based on non-linear/non-gaussian observation models. We study two different approaches, one statistical and one shape-based, to estimate the deformation of an object and track complex dynamics without learning from a training set neather the dynamical nor the deformation models and under the constraints of missing data, non-linear deformation and unknown interframe motion. The developed approaches have been successfully tested on several sequences.