优化搜索策略的KCF目标跟踪算法  

Optimized Searching Strategy for KCF Object Tracking Algorithm

作　　者：杨志方[1] 陈曦 YANG Zhifang;CHEN Xi(School of Electrical and Information Engineering,Wuhan Institute of Technology,Wuhan 4B0205,China)

机构地区：[1]武汉工程大学电气信息学院,湖北武汉430205

出　　处：《武汉工程大学学报》2019年第1期98-102,共5页Journal of Wuhan Institute of Technology

摘　　要：针对核相关滤波跟踪算法存在实时性较差的问题,提出了一种优化搜索策略的改进算法。首先,在检测到随机选取的视频某一帧中目标中心位置后,计算该目标图像块的均值和标准差。再设定一个排序队列以及两个自适应阈值来筛除一些特征与目标差异较大的候选块。在视频下一帧中,均值与标准差的差值小于设定阈值的候选块会优先检测并计算响应。实验结果表明,改进后的算法与原算法相比帧率提升可达10%左右,且跟踪精度较KCF、CSK、Struct等其它算法提升2.2%、14.4%和24.9%。To improve the real-time performance of kernelized correlation filters(KCF)algorithm,we proposedan optimized searching strategy for KCF tracking algorithm.The center position of target in a randomly selectedvideo frame was detected,and the mean value and standard deviation of the target image patch were calculatedrespectively.Then a sort queue and two adaptive thresholds were set to discard unsuitable patches with certainfeatures that differ greatly from the target patch.It was realized that the candidate patch in the next frame wasdetected and calculated with priority when whose mean values and standard deviations were both within acertain margin of the target patch.Experimental results show that the proposed algorithm increases the framerate about 10%than the original KCF algorithm,and its tracking accuracy is about 2.2%,14.4%and 24.9%higher than that of other algorithms such as KCF,circulant structure of tracking-by-detection with Kernels andStruct,respectively.

关 键 词：目标跟踪 相关滤波 候选图像块 搜索策略 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]