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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
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.
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