Probabilistic object tracking based on machine learning and importance sampling

Abstract

The paper presents a novel particle filtering framework for visual object tracking. One of the contributions is the development of a likelihood function based on one of machine learning algorithm-AdaBoost algorithm. The likelihood function can capture the structure characteristics of one class of objects, and is thus robust to clutters and noise in the complex background. The other contribution is the adoption of mean shift iteration as a proposal distribution, which can steer discrete samples towards regions which most likely contain the targets, and is therefore leading to computational efficiency in the algorithm. The effectiveness of such a framework is demonstrated with a particular class of objects-human faces. © Springer-Verlag Berlin Heidelberg 2005.