自适应模型更新的粒子滤波视觉跟踪  

Adaptive Model Updating for Particle Filter Visual Tracking

作　　者：孙巧 张胜修 曹立佳 李小锋 刘毅男[2] SUN Qiao ZHANG Sheng-xiu CAO Li-jia LI Xiao-feng LIU Yi-nan(Rocket Force University of Engineering, Xi＇an 710025, China No. 96174 Unit of PLA, Huangshan 245400, China)

机构地区：[1]火箭军工程大学 [2]中国人民解放军96174部队

出　　处：《电光与控制》2017年第2期1-5,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61203189);陕西省自然科学基金(2015JQ6226)

摘　　要：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.

关 键 词：视觉跟踪 粒子滤波 表观模型 自适应模型更新 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]