基于统计模型和活动轮廓的运动目标检测与跟踪  

Motion detection and object tracking based on statistical model and active contour

作　　者：王长军[1] 朱善安[1] 

机构地区：[1]浙江大学电气工程学院,浙江杭州310027

出　　处：《浙江大学学报（工学版）》2006年第2期249-253,共5页Journal of Zhejiang University：Engineering Science

摘　　要：提出了一种静止背景视频序列中运动目标的检测与跟踪方法.对连续两帧图像序列作差分计算,对差分图像的灰度分布建立混和高斯模型(GMM),采用期望最大化(EM)算法估计模型参数,并引入基于GMM模型的边界检测算子,进而构造运动边界图像.改进静态图像轮廓提取算法GVF-Snake,利用运动边界图像修改GVF-Snake的能量项,使其能够提取视频序列中运动目标的轮廓.采用一种根据目标区域自动初始化轮廓的方法解决Snake初始轮廓需要手工设定的问题,采用一阶差分预测算法加快轮廓收敛速度.利用改进的GVF-Snake算法对运动目标进行检测与跟踪,结果表明,该算法对刚性和非刚性两类目标都具有较好的检测与跟踪效果.An approach was proposed to detect and track moving objects in a static background video sequence. The difference of two successive frames was computed, and a Gaussian mixture model （GMM） of the gray-level distribution of the difference image was constructed, whose parameters were estimated by expectation maximization （EM） algorithm. Based on the model, a motion detection operator was introduced to generate a motion border image. Then, a static image based algorithm, GVF-Snake, was improved. The energy entry was modified by using the motion border image so that it could be used in video sequences. A method was proposed to initialize the Snake automatically, and the 1st-order difference predictive algorithm was used to accelerate the convergence of Snake. Experimental results prove that the algorithm is effective for both rigid and non-rigid ohieets.

关 键 词：运动边界检测 目标跟踪 GVF-Snaket混和高斯模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]