A Comparison Study on Kernel Based Online Learning for Moving Object Classification
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 recentlydeveloped 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.
Multi-class Moving object classification Online learning Kernel-based method Shape feature
Xin Zhao Kaiqi Huang Tieniu Tan
Department of Automation,University of Science and Technology of China,Hefei,China National Lab of P National Lab of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing,Chin
国际会议
北京
英文
17-20
2011-12-01（万方平台首次上网日期，不代表论文的发表时间）