CASIA OpenIR  > 智能感知与计算研究中心
A comparison study on kernel based online learning for moving object classification
Xin Zhao; Kaiqi Huang; Tieniu Tan
2011
Conference Name2011 Third Chinese Conference on Intelligent Visual Surveillance
Source PublicationConference on Intelligent Visual Surveillance, 2011
Pages17-20
Conference Date2011
Conference PlaceBeijing, China
AbstractMost visual surveillance and video understanding systems require knowledge of categories of objects in the scene. One of the key challenges is to be able to classify any object in a real-time procedure in spite of changes in the scene over time and the varying appearance or shape of object. In this paper, we explore the applications of kernel based online learning methods in dealing with the above problems. We evaluate the performance of recently developed kernel based online algorithms combined with the state-of-the-art local shape feature descriptor. We perform the experimental evaluation on our dataset. The experimental results demonstrate that the online algorithms can be highly accurate to the problem of moving object classification.
KeywordImage Classification   learning (Artificial Intelligence   motion Estimation
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12699
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Xin Zhao,Kaiqi Huang,Tieniu Tan. A comparison study on kernel based online learning for moving object classification[C],2011:17-20.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xin Zhao]'s Articles
[Kaiqi Huang]'s Articles
[Tieniu Tan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xin Zhao]'s Articles
[Kaiqi Huang]'s Articles
[Tieniu Tan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xin Zhao]'s Articles
[Kaiqi Huang]'s Articles
[Tieniu Tan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.