机械科学与技术2017，Vol.36Issue(1)：102-107,6.DOI:10.13433/j.cnki.1003-8728.2017.0115
CV-GAC模型与图割优化的运动目标检测和分割
Detecting and Segmenting Moving Object Using CV-GAC Model with Graph Cut Optimization
摘要
Abstract
To solve the problem of moving object tracking in video image sequences,this paper presents a novel algorithm that combines the Chan and Vese Geodesic Active Contour (CV-GAC) model with graph cut optimization.Firstly,the object's active contours are obtained by using the Gaussian mixture model and background subtraction;then,the contour of the initial curve is automatically set in the moving area,and the mathematical morphological operation is carried out by using the geodesic level set model to adaptively treat the object's topology change and globally optimize the energy function with the graph cut optimization.The experimental results show that this method effectively shortens the time for the moving object segmentation,correctly and quickly extracting the active contours of moving targets.关键词
目标检测/活动轮廓/水平集模型/运动目标/图割Key words
object tracking/geodesic active contour/geodesic level set model/moving object/graph cut optimization分类
信息技术与安全科学引用本文复制引用
宋琳,高满屯,王三民,王淑侠..CV-GAC模型与图割优化的运动目标检测和分割[J].机械科学与技术,2017,36(1):102-107,6.基金项目
国家自然科学基金项目(51105310)资助 （51105310）
