An Adaptive Kernel-based Target Tracking Method Based on Multiple Features Fusion

摘要

A new adaptive kernel-based target tracking method is proposed to improve the robustness and accuracy of target tracking in a complex background. A linear weighted combination of three kernel functions of scale-invariant feature transform (SIFT), color, and motion features is applied to represent the probability distribution of the tracked target. Appearance and motion features are combined to enhance the target region location stability and accuracy. The size of the tracking window can be adjusted in real time according to the affine transform parameters of the corresponding SIFT couples. The weights of three kernel functions are also adaptively turned according to the scene, in order to better extract the features. Experiments demonstrate that the proposed algorithm can track the moving target successfully in different scenarios. Moreover, it can handle target pose, scale, orientation, view, and illumination changes, and its performance is better than that of the classic Camshift algorithm, SIFT-based method, the and color SIFT-based method.