A Robust Real-Time Tracking Method of Fast Video Object Based on Gaussian Kernel and Random Projection

摘要

It is a challenging topic how to achieve the real-time tracking of fast video object under complex environment. In this paper, a scheme and its corresponding implementing algorithms of real-time tracking of fast video object are designed and perfected, which are characterized with high performances of real-time tracking and robustness. At first, a kind of scheme is designed for the real-time tracking of fast video object and corresponding implementing strategies for some key modules are proposed. Then the particle filter is employed to predict the pose state of fast video object and the motion object is discriminated from its background by Gaussian kernel and random projection. Moreover, an adaptive feature selection method is used to enhance the robustness and tracking efficiency. A series of experiment results demonstrate that the scheme and algorithms proposed in this paper outperform the current existing algorithms in the tracking efficiency, accuracy and robustness.