融合混合高斯模型和阈值自适应的改进Vibe算法  

An Improved Vibe Algorithm Combining Gaussian Mixture Model and Adaptive Threshold

作　　者：曹融 郝晓丽 CAO Rong;HAO Xiaoli(College of Information and Computer,Taiyuan University of Technology,Taiyuan 030600,China)

机构地区：[1]太原理工大学信息与计算机学院,太原030600

出　　处：《太原理工大学学报》2020年第4期516-521,共6页Journal of Taiyuan University of Technology

基　　金：国家重点研发计划基金资助(2017YFB1401001)。

摘　　要：针对传统的Vibe算法在运动目标检测的初期存在“鬼影”现象,以及对于其复杂环境适应性不强的问题,提出了一种改进的Vibe算法。在背景建模及初始化阶段,通过结合图像形态学处理并融合混合高斯背景模型消除“鬼影”,并在背景更新阶段引入自适应的更新半径和更新概率来提高算法精确度,使得算法可以适应多目标复杂环境。实验结果表明,在保证一定实时性的前提下,本文改进算法可快速有效地消除“鬼影”现象,并具较强的复杂环境适应性,为运动目标实时检测提供了重要参考。In order to solve the problem that the traditional vibe algorithm has"ghost"in the early stage of moving target detection and its adaptability to complex environment is not strong,an improved vibe algorithm was proposed.In the background modeling and initialization stage,the"ghost"is eliminated by combining image morphological processing and blending Gaussian background model,and the adaptive update radius and probability are introduced in the background update stage to improve the accuracy of the algorithm to adapt to multi-objective complex environment.The experimental results show that on the premise of ensuring a certain real-time performance,the improved algorithm can quickly and effectively eliminate the phenomenon of"ghost",and has a strong adaptability to the complex environment,which provides an important reference for the real-time detection of moving targets.

关 键 词：运动目标检测 Vibe算法 混合高斯 “鬼影”消除 阈值自适应 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]