基于MCD和局部线性高斯模型的视频跟踪粒子滤波算法

Particle Filter Algorithm for Visual Tracking Based on MCD and Partial Linear Gaussian Models

为了提高粒子传播过程中状态空间的质量和视频跟踪算法的精度,提出了一种基于MCD和局部线性高斯模型的粒子滤波算法,这种MCD方法摒弃了传统的像素匹配贡献均等的方式,而是采用像素点间的邻近度作为相似性度量。由于该方法所获得的相关曲面更尖锐,且匹配置信度更高,而局部线性高斯模型则可使得在粒子传播过程中能使用最佳的重要函数,因此两者结合能够实现最佳粒子滤波。由于MCD方法的鲁棒性,从而使得跟踪算法对环境的适应能力和稳定性得到提高。两个仿真实验的结果说明,该算法是可行的与优越的。

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.