文章基本信息

标题：A Novel Approach of On-line Discriminative Tracking Feature Selection
本地全文：下载
作者：Yehong Chen ; Pil Seong Park ; Aimin Li 等
期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2013
卷号：2
期号：3
页码：392
出版社：International Journal of Computer and Information Technology
摘要：We propose a framework of a multi-object tracking method based on image classification. Aiming to build an appearance model of a target, a sparse measurement matrix is used as a projection for dimensional reduction and online feature selection to improve discriminative power. Our main idea is putting appropriate weights on the features selected to form a feature pattern. Using our method, it is easy to build a multi- object tracking framework, the tracking performance of which is dependent on the power of classifiers. Since our feature selection method is motivated by experience and from the observation of experimental results, we discuss why our method works well. Experiments show good performance of our method.
关键词：Sparse Random measurement; N ; aive Bayes ; classifier; ; Feature Selection; Multi-object Tracking