针对视觉跟踪中的表观模型更新问题，在粒子滤波视觉跟踪的框架下，提出了一种基于粒子信息判据更新的自适应模型更新策略，通过挖掘粒子间的相关信息，判断跟踪状态和表观模型变化，进而构建模型更新的模糊规则表。采用著名的视觉跟踪评价数据集与经典粒子滤波视觉跟踪的模型更新方法相比较，实验结果证明了所提算法的有效性。

Aiming at the appearance model updating problem of visual tracking,an adaptive model updating strategy was proposed based on particle information criterion updating in the framework of the particle filter visual tracking.By mining the related information between the particles, analysis was made to the changes of tracking status and appearance model, and then a fuzzy rule table for model updating was built up.The method was compared with the model updating method of classical particle filter visual tracking by using the famous visual tracking evaluation data sets.The experimental results demonstrate the effectiveness of the algorithm.