基于前景分割和特征空间自适应选择的视觉目标跟踪  

Visual object tracking based on foreground segmentation and adaptive feature space selection

作　　者：高琳[1] 唐鹏[1] 盛鹏[1] 

机构地区：[1]四川大学视觉合成图形图像技术国防重点学科实验室,成都610064

出　　处：《控制与决策》2010年第2期207-212,共6页Control and Decision

基　　金：国家自然科学基金项目(60705005);国家自然科学民航联合基金重点项目(60736046);教育部博士点基金项目(20070610031);四川省应用基础研究项目(2008JY0038)

摘　　要：提出一种视觉跟踪任务中鲁棒的目标模型更新算法.首先利用结合空域信息的特征直方图对目标和背景进行建模;然后通过引入信息论中的交叉熵,定量地评价各个特征空间下目标和背景的差异性,选择差异性最大的特征空间更新目标模型,并用均值漂移算法定位目标.为解决模型更新中的偏移问题,采用条件随机场(CRF)模型,通过融合图像序列的时空上下文信息分割出目标前景,利用分割结果减小模型更新的误差.实验结果验证了该算法的有效性.A novel algorithm of object model update for visual tracking is presented. Firstly,feature histograms combined with spatial information are used to model the object and background. Then,for each feature space,the cross entropy measure of information theoretic is applied to evaluate the divergence between the distributions of object and background,and the feature space with maximal divergence is selected for tracking. The new position of the object is calculated by mean shift under the selected feature space. To alleviate the tracking drift problem,the model updating process is incorporated with the figure/ground segmentation based on systematic integration of spatial and temporal data over time by using conditional random field （CRF）. The results of the experiments performed on several sequences show the effectiveness of the proposed method.

关 键 词：特征空间 前景分割 视觉跟踪 条件随机场 交叉熵 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]