摘要
针对现在存在的基于分类的目标跟踪算法难以实现自适应目标大小变化的问题,提出并实现了基于循环核矩阵的自适应目标跟踪算法。算法首先在包含目标的感兴趣区域内采集所有的训练样本以构成一个循环矩阵结构,再使用高斯核函数构造出循环核矩阵,最后通过基于循环核矩阵的分类器的封闭形式的解进行训练和检测。同时,将比较成熟的循环矩阵理论与傅里叶分析建立连接,从而实现了在快速傅里叶变换下进行快速学习和检测。在此基础上,通过分类器对目标响应度的变化,实现自适应目标大小的变化。与一些经典的和较新的自适应目标跟踪算法进行比较,实验结果表明该算法在一定场景下能够更加准确和有效地表达目标的变化。
Classification based target tracking algorithm has the difficulty of processing size change situation of the targetadaptively, aiming at solving this problem, an adaptive target tracking algorithm based on circulant kernel matrices is proposed.The algorithm collects all the training samples in the region of interest to construct a circulant matrices structure,and uses the Gaussian circulant kernel matrice to construct a closed-form solution of the classifier and for training anddetection. At the same time, it establishes a connection between the well-established theory of circulant matrices and Fourieranalysis to realize the fast learning and detection with fast Fourier transform. On this basis, the algorithm achieves thegoal of adaptive to the size change of the target. Experimental results show that the algorithm expresses the target sizechange more accurately and effectively in some situations, when compared with some classic and new adaptive targettracking algorithms.
作者
徐少飞
刘政怡
桂斌
XU Shaofei;LIU Zhengyi;GUI Bin(College of Computer Science and Technology, Anhui University, Hefei 230601, China)
出处
《计算机工程与应用》
CSCD
北大核心
2016年第20期177-181,192,共6页
Computer Engineering and Applications
基金
安徽省科技攻关计划科技强警专项资金项目(No.1301b042020)
高等学校博士学科点专项科研基金联合资助课题(No.201133401110009)
安徽大学青年骨干教师培养对象经费资助
关键词
分类器
循环矩阵
傅里叶变换
高斯核函数
循环核矩阵
classifier
circulant matrices
Fourier transform
Gaussian kernel function
circulant kernel matrices