计算机与数字工程2019，Vol.47Issue(5)：1125-1130,6.DOI:10.3969/j.issn.1672-9722.2019.05.022
多特征融合与尺度自适应核相关滤波跟踪算法
Object Tracking Based on Kernel Correlation Filter with Multi-features Fusion and Adaptive Scale
摘要
Abstract
To deal with the limitations of using single feature and fixed scale in the traditional Kernelized Correlation Filters, an adaptive tracking algorithm based on KCF with multi-features fusion and adaptive scale is proposed. The feature map of the target is combined color attribute and HOG features. The computation PCA is used to obtain the discriminative CN features. The adaptive scale filter is designed to dynamically adjust the target scale. The scale filter and translation filter are trained and optimized indepen?dently. This proposed tracking algorithm is robust and real-time. Moreover,it performs better than other KCF in complex factors, such as appearance variety,scale variation,illumination variation and so on.关键词
目标跟踪/核相关滤波/尺度自适应/特征融合Key words
object tracking/kernelized correlation filters/adaptive scale/feature fusion分类
信息技术与安全科学引用本文复制引用
冯汉,王永雄,张孙杰..多特征融合与尺度自适应核相关滤波跟踪算法[J].计算机与数字工程,2019,47(5):1125-1130,6.基金项目
国家自然科学基金项目(编号:61673276,61603255,61703255)资助. （编号:61673276,61603255,61703255）
