文章基本信息

标题：An Object Tracking Algorithm Based on the "Current" Statistical Model and the Multi-Feature Fusion
本地全文：下载
作者：Wang, Jinhua ; Cao, Jie ; Wu, Di 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2012
卷号：7
期号：9
页码：2000-2008
DOI：10.4304/jsw.7.9.2000-2008
语种：English
出版社：Academy Publisher
摘要：Aimed at accurary and real-time object tracking under complex background,an object tracking algorithm based on multi feature fusion is proposed. Feature points tracking is used to reduce the match time and improve the real-time of tracking; To overcome the inaccuracy of a single feature tracking, the object model is presented by the color and texture features. For the traditional "current" statistical model in maneuvering object tracking defects, an improved algorithm which combined with adaptive kalman filter (AKF) is proposed to improve the tracking accuracy. Experimental results show that the proposed method is effective and robust under complex background, the object is similar to each other, the target was partial occlusion and when the object is maneuvering.
关键词：object tracking;objective model;“current” statistical model;feature points;feature fusion