A Probabilistic Model-Based Template Matching Approach for Robust Object Tracking in Real-Time

摘要

In recent years, template matching approaches for object tracking in real-time have become more and more popular, mainly due to the increase in available computational power and the advent of very efficient algorithms. Particularly, data-driven methods based on first order approximations have shown very promising results. If the object to be tracked is known, a model-based tracking algorithm is preferable, because available knowledge of the appearence of the object from different views can be used to improve the robustness of tracking. In this paper, we enhance the well-known hyperplane tracker with a probabilistic tracking framework using the CONDENSATION algorithm, which is noted for its robustness and efficiency. Furthermore, we put forward a subspace method for improving the tracker's robustness against illumination variations. We prove the efficiency of our proposed methods with experiments on video sequences of real scenes with cluttered background and arbitrary movements of the tracked object.