Adaboost检测和混合粒子滤波融合的多目标跟踪  

Multi-target tracking of Adaboost detection combining with hybrid particle filtering

作　　者：初红霞[1] 秦进平[1] 谢忠玉[1] 张荣沂[1] 

机构地区：[1]黑龙江工程学院电气与信息工程学院,黑龙江哈尔滨150050

出　　处：《华中科技大学学报（自然科学版）》2013年第7期76-81,共6页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：黑龙江省教育厅科学技术研究项目(12531528);黑龙江省自然科学基金资助项目(QC2011C060);黑龙江工程学院博士科学研究基金资助项目(2012BJ20)

摘　　要：针对多目标跟踪时因存在很多不确定性因素,而导致粒子滤波不能有效处理多模式的增长问题,首先,提出一种混合粒子滤波跟踪方法,通过混合权值的计算实现粒子间的相互关联,能有效地保持和处理多模式问题;其次,为了提高算法对数目变化的多目标跟踪处理能力,在混合粒子滤波跟踪算法中,又融入了Ada-boost检测算法,用动态模型和Adaboost检测信息合并成的混合观测模型构造似然函数,实现了一种能学习、检测和跟踪感兴趣目标的跟踪系统;最后,在刚性、非刚性以及数目变化的多目标视频序列中对算法进行测试,结果表明算法对数目变化的多目标能实现有效跟踪.To reduce the uncertainties of multi-target tracking,a hybrid particle filter method was proposed.The interconnection was realized among particles through the calculation of hybrid weights,which could effectively maintain and deal with multi-mode problems.Secondly,in order to improve the mul-targets tracking processing ability of the algorithm when the number of multi-target changed,the Adaboost detection algorithm was integrated into hybrid filter tracking algorithm.Likelihood function was constructed through hybrid measurement model which was merged between the dynamic model and Adaboost detection.Then a tracking system to learn,detect and track interesting target was realized.Finally,the algorithm was tested in multi-target video sequence of a rigid,non-rigid and number-changing.The experiment shows that the algorithm can effectively track multi-targets changing in numbers.

关 键 词：图像处理 多目标 跟踪 混合粒子滤波 Adaboost检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]