Bayesian kernel tracking

Abstract

We present a Bayesian approach to real-time object tracking using nonparametric density estimation. The target model and candidates are represented by probability densities in the joint spatial-intensity domain. The new location and appearance of the target are jointly derived by computing the maximum likelihood estimate of the parameter vector that characterizes the transformation from the candidate to the model. This probabilistic formulation accommodates variations in the target appearance, while being robust to outliers represented by partial occlusions. In this paper we analyze the simplest parameterization represented by translation in both domains and present a gradient-based iterative solution. Various tracking sequences demonstrate the superior behavior of the method. © Springer-Verlag Berlin Heidelberg 2002.