A comparison study on kernel based online learning for moving object classification

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
会议名称	2011 Third Chinese Conference on Intelligent Visual Surveillance
会议录名称	Conference on Intelligent Visual Surveillance, 2011
页码	17-20
会议日期	2011
会议地点	Beijing, China
摘要	Most 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.
关键词	Image Classification learning (Artificial Intelligence motion Estimation
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12699
专题	智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 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.