西安理工大学学报2017，Vol.33Issue(3)：259-264,6.DOI:10.19322/j.cnki.issn.1006-4710.2017.03.002
基于多模态词典学习的目标跟踪算法
Object tracking based on multi-modality dictionary learning
摘要
Abstract
Sparse representation-based methods have been successfully applied to visual tracking.However,the over complete dictionary mode is single and data is large,and the sparse coefficients need to be solved by the complex optimization algorithm,which will limit their tracking performances.In this paper,within the tracking framework of particle filter,we propose a tracking method based on the multi-modality dictionary learning.Firstly,a long and short period of object templates are created,combined with background templates to form a multi-modality dictionary to characterize the current state of sampled object.Secondly,according to the multi-modal coefficients between the sampled objects and the dictionary,the target is tracked roughly with the candidate tracking results obtained.Finally,the observation likelihood functions of the candidate tracking results and the multi-modality dictionary are constructed by using LOMO features,and the candiadate tracking result with the maximum likelihood is taken as the final tracking result.Experimental results demonstrate that the proposed method has strong tracking robustness in the case of occlusion,illumination change and background interference.关键词
目标跟踪/多模态词典/粒子滤波/稀疏表示Key words
object tracking/multi-modality dictionary/particle filter/sparse representation分类
信息技术与安全科学引用本文复制引用
王婧,朱虹..基于多模态词典学习的目标跟踪算法[J].西安理工大学学报,2017,33(3):259-264,6.基金项目
国家自然科学基金资助项目(61673318,61771386) （61673318,61771386）
陕西省自然科学基础研究计划资助项目(2016JM6045) （2016JM6045）
陕西省教育厅科学研究计划专项资助项目(16JK1571) （16JK1571）
