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
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.