【摘要】 均值漂移(Meanshift)是一种鲁棒的快速模式匹配算法,但该算法框架下现有的整体模型更新策略不足以对场景中目标外观变化、遮挡等情况进行有效处理.为此,本文提出了一种Meanshift框架下的选择性子模型更新策略,将特征模型中的每个分量作为单独个体,基于每个分量的匹配贡献度,分别选择当前帧中需要更新的子模型分量及其更新权值.实验结果表明本文算法具有比整体模型更新策略更好的跟踪鲁棒性.更多还原

【Abstract】 Mean shift is a robust and real-time pattern matching algorithm.At present,the total model update strategy of the mean shift algorithm still has shortage under changed scenes,e.g.,target appearance changes,non-target occlusion.Therefore,the paper presents a selective sub-model update strategy for the mean shift algorithm.The proposed method treats each sub-model of target model as singleton,selects and updates sub-model and its weight based on match contributing degree of each sub-model in the current frame.The experiment result shows the proposed method is more robust and effective than the total model update strategy.更多还原