卢湖川

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:未来技术学院/人工智能学院执行院长

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:信息与通信工程学院

学科:信号与信息处理

办公地点:大连理工大学未来技术学院/人工智能学院218

联系方式:****

电子邮箱:lhchuan@dlut.edu.cn

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

Online Object Tracking With Sparse Prototypes

点击次数:

论文类型:期刊论文

发表时间:2013-01-01

发表刊物:IEEE TRANSACTIONS ON IMAGE PROCESSING

收录刊物:SCIE、EI、PubMed、ESI高被引论文、Scopus

卷号:22

期号:1

页面范围:314-325

ISSN号:1057-7149

关键字:Appearance model; l(1) minimization; object tracking; principal component analysis (PCA); sparse prototypes

摘要:Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l(1) regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.