邱贺磊,
郑佳,
裴炳南
大连大学信息工程学院 ??大连 ??116622
基金项目:国家自然科学基金(61301258, 61271379)，中国博士后科学基金(2016M590218)
详细信息
作者简介:王洪雁：男，1979年生，副教授，博士，主要研究方向为MIMO雷达信号处理、毫米波通信、机器视觉
邱贺磊：男，1991年生，硕士生，研究方向为图像处理、机器视觉
郑佳：男，1990年生，硕士生，研究方向为机器视觉、无人机容错控制
裴炳南：男，1956年生，教授，博士，博士生导师，主要研究方向为雷达信号处理、毫米波通信
通讯作者:王洪雁 gglongs@163.com
中图分类号:TP391计量
文章访问数:1109
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被引次数:0
出版历程
收稿日期:2018-05-10
修回日期:2018-11-08
网络出版日期:2018-11-19
刊出日期:2019-03-01
Visual Tracking Method Based on Reverse Sparse Representation under Illumination Variation
Hongyan WANG,,Helei QIU,
Jia ZHENG,
Bingnan PEI
College of Information Engineering, Dalian University, Dalian 116622, China
Funds:The National Natural Science Foundation of China (61301258, 61271379), China Postdoctoral Science Foundation (2016M590218)
摘要
摘要:针对光照变化引起目标跟踪性能显著下降的问题，该文提出一种联合优化光照补偿和多任务逆向稀疏表示的视觉跟踪方法。首先基于模板与候选目标的平均亮度差异对模板实施光照补偿，并利用候选目标逆向稀疏表示光照补偿后的模板。而后将所得多个关于单模板的优化问题转化为一个关于多模板的多任务优化问题，并利用交替迭代方法求解此多任务优化问题以获得最优光照补偿系数矩阵以及稀疏编码矩阵。最后利用所得稀疏编码矩阵快速剔除无关候选目标，并采用局部结构化评估方法实现目标精确跟踪。仿真结果表明，与现有主流算法相比，剧烈光照变化情况下，所提方法可显著改善目标跟踪精度及稳健性。
关键词:视觉跟踪/
光照补偿/
稀疏表示/
粒子滤波
Abstract:Focusing on the issue of heavy decrease of object tracking performance induced by illumination variation, a visual tracking method via jointly optimizing the illumination compensation and multi-task reverse sparse representation is proposed. The template illumination is firstly compensated by the developed algorithm, which is based on the average brightness difference between templates and candidates. In what follows, the candidate set is exploited to sparsely represent the templates after illumination compensation. Subsequently, the obtained multiple optimization issues associated with single template can be recast as a multi-task optimization one related to multiple templates, which can be solved by the alternative iteration approach to acquire the optimal illumination compensation coefficient and the sparse coding matrix. Finally, the obtained sparse coding matrix can be exploited to quickly eliminate the unrelated candidates, afterwards the local structured evaluation method is employed to achieve the accurate object tracking. As compared to the existing state-of-the-art algorithms, simulation results show that the proposed algorithm can improve the accuracy and robustness of the object tracking significantly in the presence of heavy illumination variation.
Key words:Visual tracking/
Illumination compensation/
Sparse representation/
Particle filter
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