%0 Journal Article
%T Multi-feature fusion tracking based on new nonlinear filtering<br>一种新型非线性滤波的多特征融合跟踪算法
%A QI Hong-biao
%A LI Wei
%A <br>亓洪标
%A 李伟
%J 计算机应用研究
%D 2012
%I 
%X This paper proposed a new kind of nonlinear filtering for the state estimation of nonlinear systems. The proposed algorithm based on quadrature Kalman filter by using integral pruning factor, which optimized and reorganized the integration point. New algorithm overcame the particle degeneration phenomenon well. In the improving particle filter framework, this algorithm used color and motion edge character as observation model, and fused feature weights through the D-S evidence theory. The proposed method effectively avoided bad robust questions rosed by the single color feature in the posture change and similar feature occlusion. Experiment results indicate that the proposed method is more robust to track object in complex scene and the tracking precision ascends nearly 32%.
%K particle filter
%K quadrature Kalman filter
%K object tracking
%K multi-feature fusion
%K D-S evidence theory<br>粒子滤波
%K 积分卡尔曼滤波
%K 目标跟踪
%K 多特征融合
%K DS证据理论
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F886A295C1E57CC507A049BAAFEF3D8B&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=D3EB3151C2B04CB9&eid=76F79DF7CA22520E&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=16