Multisensor long distance target detection using support vector machine

作     者：杨烜 裴继红 

作者机构：College of Information Engineering Shenzhen UniversityShenzhen 518060 Intelligent Information Processing Institute of Shenzhen University Shenzhen 518060 

出 版 物：《Chinese Optics Letters》 (中国光学快报（英文版）)

年 卷 期：2007年第5卷第7期

页      面：400-402页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Defense Key Laboratory Fundation under Grant No.51483040105QT5118 

主　　题：Infrared imaging Support vector machines Target tracking 

摘      要：Multisensor image fusion could improve system performances such as detection, tracking, and identification greatly. In this paper, a long distance target detection approach is presented based on multisensor image features fusion. This method extracts two different features from visual and infrared （IR） image sequences respectively to detect regions of motion information content. Temporal change feature is extracted from the visual image sequence using temporal decomposition based on wavelet, which reflects the dynamical content variation at a pixel at any time. And correlation features between local regions are extracted from IR image sequence to distinguish regions with potential moving targets. All these features are merged into a multi-dimensional space and the support vector machine is trained to select regions that have the potential target at each pixel location. The method is robust and feasible to detect long distance targets in clutter background scene.