复杂背景下的红外目标自动跟踪算法  

Auto-tracking Algorithm of Infrared Target under Complex Background

作　　者：高国旺[1,2] 刘上乾[1] 秦翰林[1] 张峰[1] 

机构地区：[1]西安电子科技大学技术物理学院,西安710071 [2]西安石油大学光电油气测井与检测教育部重点实验室,西安710065

出　　处：《光电工程》2010年第6期78-83,共6页Opto-Electronic Engineering

基　　金："十一五"国防预研基金项目

摘　　要：提出了一种非线性边缘检测和Mean Shift方法相结合的红外目标检测与跟踪算法。采用双窗口算子的非线性边缘检测算法具有计算量小、速度快、图像质量好等优点。在边缘检测后的二值图像基础上,利用改进的Mean Shift跟踪算法实施目标跟踪。该跟踪算法融合了计算目标区域局部标准差的信息;利用灰度值和局部标准差的概率密度函数来描述目标;同时选择核函数级联方式进行目标密度估计,从而弥补了仅用灰度信息描述目标特征的不足。实验结果表明,该跟踪算法检测出的复杂背景下红外目标边缘清晰,并且能够准确地对目标实施自动跟踪。A tracking algorithm of infrared target is proposed by combining non-linear edge detection and Mean Shift method.The non-linear edge detection algorithm employs dual-window arithmetic operators that have the advantage of few calculation amount,high speed and good image quality and so on.The result of edge detection is binary images.Based on this information,the Mean Shift method is improved to implement target tracking.The tracking algorithm of improved Mean Shift combines the information of the local standard deviation calculation of the target area,describes the target based on the probability density function about gray value and the local standard deviation and selects cascade kernel function to calculate the target density that make up the shortage only using gray to describe the target features.Experimental results show that the edge of infrared target under complex background is detected clearly and infrared target is auto-tracked accurately.

关 键 词：红外目标 边缘检测 Mean Shift方法 自动跟踪 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]