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运动摄像机下多目标检测与跟踪
引用本文:陈李迪超,郭继昌.运动摄像机下多目标检测与跟踪[J].计算机工程,2012,38(20):132-135.
作者姓名:陈李迪超  郭继昌
作者单位:天津大学电子信息工程学院,天津,300072
基金项目:天津市科技支撑计划基金资助项目(10ZCKFGX00700)
摘    要:针对摄像机运动情况下多目标的检测与跟踪问题,提出一种将Global K均值与模板匹配相结合的方法.利用六参数仿射模型得到摄像机运动参数,对图像进行全局运动补偿,用GlobalK均值算法对前景点进行循环聚类,判断目标数目并进行跟踪,通过对目标区域进行模板匹配使跟踪结果更准确.实验结果表明,该方法能够在运动摄像机下稳定、实时地跟踪多个目标,对发生形变的目标基本也能稳定跟踪.

关 键 词:运动摄像机  多目标  跟踪  仿射变换模型  K均值
收稿时间:2011-11-30
修稿时间:2012-01-31

Detection and Tracking of Multi-object Under Moving Camera
CHEN Li-di-chao , GUO Ji-chang.Detection and Tracking of Multi-object Under Moving Camera[J].Computer Engineering,2012,38(20):132-135.
Authors:CHEN Li-di-chao  GUO Ji-chang
Affiliation:(School of Electronic Information Engineering,Tianjin University,Tianjin 300072,China)
Abstract:In order to solve the problem of multi-object detection and tracking in real-time with the moving camera,this paper uses Global K-means based on affine transform model along with template matching.It adopts the affine transform model to get the global motion and compensates it between images.The Global K-means method is used to iteratively cluster the foreground points and decide the numbers of the objects to track.The template matching of objects regions makes the tracking more accurate.Experimental results demonstrate that the method can track multiple-object steadily in real-time with moving camera.
Keywords:moving camera  multi-object  tracking  affine transforming model  K-means
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