Particle Filter Algorithm for Visual Tracking Based on MCD and Partial Linear Gaussian Models
基于MCD和局部线性高斯模型的视频跟踪粒子滤波算法

In order to improve the quality of the state-space exploration and the accuracy of visual tracking,in this paper a particle filter algorithm based on maximum close distance(MCD) and partial linear Gaussian models is presented.MCD avoids the problem that each pair of pixels in the image contribute to the matching result equally.The proposed method uses neighborhood between pixels as the matching similarity.The correlation curve obtained in this way is much sharper.So the image matching method has high matching precision.A direct consequence of using partial linear Gaussian models is that the optimal importance function is adopted.The combination of them will be the optimal particle filter.The stability of the algorithm has been improved due to the robustness of MCD.Two simulated experiments are finally carried out to confirm the validity of the improved algorithm.