基于混合粒子滤波和稀疏表示的目标跟踪算法

针对图像序列中的运动目标在跟踪过程中易受到光照等复杂环境、外观变化及部分遮挡影响的问题，提出基于全局信息和局部信息的混合粒子滤波算法.将目标的局部二元模式纹理特征引入粒子滤波算法，通过稀疏编码目标子块，充分利用目标的局部空间信息，并结合全局信息以确定当前帧中目标的位置.在跟踪过程中实时更新模板，这在一定程度上提高算法的鲁棒性.实验表明在目标处于复杂环境中算法能达到较理想的跟踪效果.

Object Tracking Algorithm Based on Hybrid Particle Filter and Sparse Representation

To reduce the influence of complex environment like illumination variation, appearance change and partial occlusion during the object tracking in the sequence images, a hybrid particle filter tracking method based on global and local information is proposed. The local binary patterns (LBP) textual feature is introduced into the particle filter algorithm. Through sparse coding target sub-block, the local information is fully used, and the global information is taken into account to determine the position of target in the current frame. During the tracking, the robustness of the tracking algorithm is improved since the template is updated in real time. Experimental results show that the proposed tracking algorithm achieves good results in complex background.