传感技术学报2018，Vol.31Issue(1)：61-67,7.DOI:10.3969/j.issn.1004-1699.2018.01.011
基于重要性加权的结构稀疏跟踪方法
Structural Sparse Tracking Method Based on Importance Weighting
摘要
Abstract
According to the limitations of describe target capabilities and the effectiveness of local sparse representa-tion model in visual tracking,a structural sparse tracking method based on importance weighting is proposed aimed to the deficiencies. In the method,we adopt sparse representation to model object and according to important degree of expressing object,each local image is weighted to improve robustness of target model. In the framework of particle filtering,based on maximum a posteriori probability to estimate target. In addition,a template updating strategy of occlusion detection mechanism is used to real-time update template to avoid tracking drift. Experimental results show that the proposed method can effectively reduce the influence of the object apparent change on the model and our method is competitive to the state-of-the-art trackers on challenging video sequences with illumination changes, background clutter,occlusion,fast motion and deformation.关键词
目标跟踪/稀疏表示/重要性加权/目标表观Key words
object tracking/sparse representation/importance weighting/target apparent分类
信息技术与安全科学引用本文复制引用
梁贵书,牛为华,李宝树,李强,赵鹏..基于重要性加权的结构稀疏跟踪方法[J].传感技术学报,2018,31(1):61-67,7.基金项目
项目来源:中央高校基本科研业务费专项项目( 2017MS156) （ 2017MS156）
