再生核Hilbert空间中两阶段稀疏表示目标跟踪算法  

Two-stage sparse representation objective tracking algorithm in reproducing kernel Hilbert space

作　　者：朱虎飞 丁子豪 杨永亮[1] 冯旭祥 丁大伟[1] ZHU Hu-fei;DING Zi-hao;YANG Yong-liang;FENG Xu-xiang;DING Da-wei(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;School of Automation,Beijing Institute of Technology,Beijing 100081,China;Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]北京理工大学自动化学院,北京100081 [3]中国科学院空天信息创新研究院,北京100094

出　　处：《控制理论与应用》2022年第4期730-740,共11页Control Theory & Applications

基　　金：国家自然科学基金项目(61903028,61873028)资助。

摘　　要：在强干扰复杂环境下,有效的特征选择对于目标跟踪模型的可解释性至关重要.针对这一问题,本文基于再生核Hilbert空间(RKHS)理论,对特征空间构建生成式的两阶段稀疏表示(TSSR)模型,从而描述图像样本与字典之间的非线性关系,避免了在字典中引入大量的琐碎模板.在第1阶段,首先建立图像样本与字典在原始低维空间中的关系,然后利用批处理最小二乘算法求得稀疏表示系数的初值,根据观测模型确定初始跟踪位置的分布;在第2阶段,首先利用核方法将原始低维空间映射到高维特征空间,然后提出一种基于核的加速近端梯度算法(KAPG),从而求得字典元素系数的核稀疏表示,最终确定跟踪目标.最后实验结果证明了本文所提出的TSSR方法在面对视角变化和部分遮挡时的有效性.In the complex environment with strong interference,the effective feature selection is crucial for the objective tracking model interpretability.To tackle this issue,based on the reproducing kernel Hilbert space(RKHS)theory,this paper constructs a generative two-stage sparse representation(TSSR)model in the feature space to describe the nonlinear relationship between the image sample and the dictionary,while avoiding the introduction of a large size of trivial templates.In the first stage,we establish the relationship between the image sample and the dictionary in the original low-dimensional space,then the batch least square algorithm is used to obtain the initial value of the sparse representation coefficient.The distribution of the initial tracking position is determined according to the observation model.In the second stage,we use the kernel method to map the low-dimensional original space to the high-dimensional feature space,and then propose a kernel-based accelerated proximal gradient algorithm(KAPG)to obtain the sparse representation of the dictionary element coefficients.On this basis,the tracking target can be determined.The experimental results show the effectiveness of the proposed TSSR method in the face of viewing angle changes and partial occlusion.

关 键 词：目标跟踪 再生核HILBERT空间 核方法 稀疏表示 两阶段框架 加速近端梯度算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]