一种基于核相关滤波的视觉跟踪算法  

Visual Tracking Algorithm Based on Kernelized Correlation Filter

作　　者：黄健 郭志波[1] 林科军 HUANG Jian;GUO Zhi-bo;LIN Ke-jun(School of Information Engineering,Yangzhou University,Yangzhou,Jiangsu 225127,China)

机构地区：[1]扬州大学信息工程学院,江苏扬州225127

出　　处：《计算机科学》2018年第B11期230-233,共4页Computer Science

基　　金：江苏省前瞻性联合研究项目(BY2015061-01)资助

摘　　要：视觉跟踪是计算机视觉的一个重要方向,而核相关滤波(KCF)跟踪是视觉跟踪领域中的一种比较新颖的方法,它不同于传统基于目标特征的方法,不仅具有较高的跟踪精度,而且具有较快的跟踪速度,在实际应用中效果显著。但当物体快速运动或存在较大尺度变化等时,该方法无法准确地跟踪目标。文中提出的基于核相关滤波器的改进算法有效地解决了上述问题,其通过随机更新多模板匹配,确定了核相关滤波的学习因子,从而实现了学习因子自适应更新模型。实验结果表明,该算法根据不同的场景能快速地调整学习因子,从而提高跟踪的成功度。通过自适应学习因子和多模板匹配,该算法对部分遮挡、光照和目标尺度变化具有较强的适应性。Visual tracking is an important part of the computer vision,and kernelized correlation filter tracking is a relatively novel method in visual tracking field.It is different from traditional method based on target feature,which has high accuracy and fast tracking speed.However,when the object moves rapidly or has the larger scale changes,the method cannot track the target accurately.This paper proposed an improved algorithm based on the correlative filter which can effectively overcome the above problems.The learning factors of kernelized correlation filtering and the ada-ptive updating model of learning factors are determined by using random update multi-template matching.Experimental results show that the algorithm can adjust the learning factors quickly according to different scenarios,thus the success rate of tracking will be improved.Through adaptive learning factor and multi-template matching,this algorithm has robust adaptability to partial occlusion,illumination and target scale.

关 键 词：目标跟踪 核相关滤波 多模板匹配 随机更新 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]