适应目标尺度变化的改进压缩跟踪算法  

An Improved Compressive Tracking Algorithm Adapting to Variable Target Scales

作　　者：张雨婷[1] 叶东毅[1] 柯逍[1] 陈昭炯[1] 

机构地区：[1]福州大学数学与计算机科学学院,福州350116

出　　处：《模式识别与人工智能》2016年第11期985-996,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61502105)资助~~

摘　　要：基于压缩感知理论对目标Haar-like特征进行降维处理的压缩跟踪算法采用固定大小的跟踪框跟踪目标,在目标尺度发生变化时,容易产生跟踪漂移甚至丢失跟踪目标的现象.为了克服这一缺陷,文中分析Haar-like特征随目标尺度变化的情况,发现在一定变化尺度范围内,跟踪矩形框内目标Haar-like特征值的变化与跟踪矩形框的面积变化呈近似线性关系,在此基础上提出适应目标尺度变化的改进压缩跟踪算法(CTVS).实验表明,CTVS具有较高的尺度自适应能力,能更好地减轻目标跟踪过程中可能出现的遮挡、光照变化、背景混杂、变形等干扰因素的影响,具有较高的鲁棒性和准确性.同时算法计算效率较高,能够达到实时跟踪的目的.Compressive tracking algorithms based on compressive sensing theory for reducing the dimension oi Haar-like feature of the target utilize a fixed tracking scale, and therefore they are prone to tracking drift or even target missing when the size of the target changes. To overcome the drawback, the variation of Haar-like feature according to the target scales is analyzed. It is found that the values of Haar-like feature of target in the tracking rectangular frame change with the area of the tracking frame in an approximately linear way within certain range of scales. Grounded on this relationship, an improved compressive tracking algorithm adapting to variable target scales （CTVS） is proposed. Experimental results show that CTVS can adapt to the change of target size and perform well in reducing the influence of interferences like occlusion, light illumination variation, background clutter and deformation. Moreover, CTVS is capable of real-time tracking with higher robustness, accuracy and computation efficiency.

关 键 词：目标跟踪 压缩感知 HAAR-LIKE特征 尺度变化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]