多特征联合的稀疏跟踪方法

doi:10.11772/j.issn.1001-9081.2014.08.2380

计算机应用 ›› 2014, Vol. 34 ›› Issue (8): 2380-2384.DOI: 10.11772/j.issn.1001-9081.2014.08.2380

多特征联合的稀疏跟踪方法

胡昭华¹,²,徐玉伟¹,赵孝磊¹,何军¹,²

1. 南京信息工程大学电子与信息工程学院，南京210044
2. 南京信息工程大学江苏省气象探测与信息处理重点实验室，南京210044

收稿日期:2014-02-27 修回日期:2014-04-21 出版日期:2014-08-01 发布日期:2014-08-10
通讯作者: 胡昭华
作者简介:胡昭华（1981-），女，安徽马鞍山人，副教授，博士，主要研究方向：视频目标跟踪、模式识别、粒子滤波；徐玉伟（1987-），男，江苏阜宁人，硕士研究生，主要研究方向：视频目标跟踪；赵孝磊（1988-），男，河南信阳人，硕士研究生，主要研究方向：人脸识别；何军（1978-），男，河南郑州人，讲师，博士，主要研究方向：计算机视觉、高维数据分析、压缩感知。
基金资助:
国家自然科学基金青年科学基金资助项目;江苏省普通高校自然科学研究资助项目;江苏高校优势学科建设工程资助项目;江苏省自然科学基金青年基金资助项目

Sparse tracking algorithm based on multi-feature fusion

HU Shaohua¹,²,XU Yuwei²,ZHAO Xiaolei²,HE Jun¹,²

1. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
2. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China

Received:2014-02-27 Revised:2014-04-21 Online:2014-08-01 Published:2014-08-10
Contact: HU Shaohua

摘要/Abstract

摘要：

针对目标跟踪中单一特征描述目标能力较弱的情况，提出一种多种特征联合的稀疏表示跟踪方法。在粒子滤波框架下，首先，提取目标模板和候选粒子的多种特征并对其进行核化处理；然后，用字典模板对各候选粒子进行联合稀疏表示，采用可核化的加速近端梯度(KAPG)方法求解稀疏系数并实现候选粒子的重构；最后，将具有最小重构误差的粒子作为跟踪结果。跟踪过程中，利用子空间学习的方法实现目标模板的更新。实验结果表明，与现有跟踪算法相比，该算法提高了跟踪精度，并在目标存在遮挡、光照变化、运动突变等情况时，均可以取得较好的跟踪效果。

Abstract:

This paper proposed a novel sparse tracking method based on multi-feature fusion to compensate for incomplete description of single feature. Firstly, to fuse various features, multiple feature descriptors of dictionary templates and particle candidates were encoded as the form of kernel matrices. Secondly, every candidate particle was sparsely represented as a linear combination of all atoms of dictionary. Then the sparse representation model was efficiently solved using a Kernelizable Accelerated Proximal Gradient (KAPG) method. Lastly, in the framework of particle filter, the weights of particles were determined by sparse coefficient reconstruction errors to realize tracking. In the tracking step, a template update strategy which employed incremental subspace learning was introduced. The experimental results show that, compared with the related state-of-the-art methods, this algorithm improves the tracking accuracy under all kinds of factors such as occlusions, illumination changes, pose changes, background clutter and viewpoint variation.

中图分类号:

胡昭华徐玉伟赵孝磊何军. 多特征联合的稀疏跟踪方法[J]. 计算机应用, 2014, 34(8): 2380-2384.

HU Shaohua XU Yuwei ZHAO Xiaolei HE Jun. Sparse tracking algorithm based on multi-feature fusion[J]. Journal of Computer Applications, 2014, 34(8): 2380-2384.

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多特征联合的稀疏跟踪方法

Sparse tracking algorithm based on multi-feature fusion

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