尺度自适应特征压缩跟踪  

The scale adaptive feature compressed tracking

作　　者：张路平[1] 韩建涛[1] 李飚[1] 王鲁平[1] 

机构地区：[1]国防科技大学电子科学与工程学院,湖南长沙410073

出　　处：《国防科技大学学报》2013年第5期146-151,共6页Journal of National University of Defense Technology

基　　金：国家部委资助项目;国家863计划资助项目

摘　　要：为了对复杂环境中的目标进行长时间精确跟踪,提出一种尺度自适应特征压缩跟踪算法。通过结构约束性采样,获取不同位置不同尺度的扫描窗,离线计算不同尺度下的稀疏随机感知矩阵。在线跟踪时利用这些矩阵感知对应尺度的图像采样块,实现特征降维,提高运算速度。利用朴素贝叶斯分类器对降维特征进行判决,在线学习更新分类器参数,找出具有最高分类得分的采样块及其尺度作为新的跟踪结果,实现跟踪位置及尺度的自适应更新。实验结果表明,该算法能适应目标的基本姿态变化及尺度缩放,不依赖于目标初始跟踪区域尺度选取,跟踪结果具有较强的鲁棒性。In order to track target accurately during a long term in complicated environment, an adaptive scale feature compressed tracking algorithm is presented. A number of scanning windows with different scales and positions were obtained by construction constraint sampling. To reduce the feature dimensiwa and improve the processing speed, the sparse random perceived matrices of different scales which can be easily computed offline were adopted to extract the features of different sampling image patches with relevant scales online. The sampling patch having a maximal classification score was regarded as the new tracking result by classifying the compressing feature via a naive bayes classifier and updating the parameters through online learning, which can realize the adaptive update of tracking location and scales. Experimental results show that the algorithm can adapt itself to the basic attitude and scale change, which is robust and does not depend on the scale selection of the initial tracking area.

关 键 词：特征压缩跟踪 尺度自适应 结构约束性采样 稀疏随机感知矩阵 朴素贝叶斯分类器 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]