摘要
提出了一种运动目标检测与跟踪算法。以每点色彩信息的混合高斯模型来实现对视频图像的背景估计,从而实现对运动目标的检测。利用模板匹配的方法实现对运动目标的跟踪,并对混合高斯模型的算法进行了改进,使其能更快、更有效地适应环境的变化。实验结果表明此方法具有较强的鲁棒性,能较好地适应各种气候和光照环境的变化。
An approach to detecting and tracking a moving object is presented. Foreground objects are segmented by using a per-pixel color-based hybrid Gaussian model as the background updating method, and tracked by template matching. A new method which improves the hybrid Gaussian model is presented, which allows the system to learn faster and more accurately and adapt effectively to the changing environments. Experiments show that the method is more robust than the state-of-the-art without sacrificing the real-time performance and well-suited for various climates and lighting conditions.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2005年第3期419-421,共3页
Systems Engineering and Electronics
基金
国家自然科学基金资助课题(60175011)
关键词
混合高斯模型
卡尔曼滤波
运动目标检测
模板匹配
hybrid Gaussian model
Kalman filtering
moving object detection
template matching
