模式识别与人工智能
Friday, Apr. 4, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2017, Vol. 30 Issue (4): 302-313    DOI: 10.16451/j.cnki.issn1003-6059.201704002
Papers and Reports Current Issue| Next Issue| Archive| Adv Search |
Visual Tracking via Hierarchical Extreme Learning Machine and Local Sparse Model
SUN Rui, ZHANG Dongdong, GAO Jun
School of Computer and Information, Hefei University of Technology, Hefei 230009

Download: PDF (2957 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  To address problems of appearance change and partial occlusion in target tracking, a tracking algorithm is presented via combing hierarchical extreme learning machine(HELM) and adaptive structural local sparse appearance model(ASLSAM). HELM is capable of extracting robust features and fast classification. ASLSAM can improve the tracking accuracy and handle the partial occlusion. Finally, results of both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the tracking process of the proposed algorithm is stable with high tacking precision.
Key wordsVisual Tracking      Hierarchical Extreme Learning Machine(HELM)      Local Sparse Appearance Model(LSAM)      Deformation      Partial Occlusion     
Received: 22 August 2016     
Fund:Supported by National Natural Science Foundation of China(No.61471154)
About author:: (SUN Rui, born in 1976, Ph.D., professor. His research interests include computer vision and machine learning.)
(ZHANG Dongdong(Corresponding author), born in 1992, master student. His research interests include computer vision and target tracking.)
(GAO Jun, born in 1963, Ph.D., profe-
ssor. His research interests include intelligent information processing and pattern recognition.)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
SUN Rui
ZHANG Dongdong
GAO Jun
Cite this article:   
SUN Rui,ZHANG Dongdong,GAO Jun. Visual Tracking via Hierarchical Extreme Learning Machine and Local Sparse Model[J]. , 2017, 30(4): 302-313.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704002      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/302
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn