Abstract: In order to solve the traditional Gaussian mixture model’s problem which has the low speed of background modeling and high computational complexity,this paper puts forward a moving target detection method.This method can be divided into two steps.Improve the update’s process of the traditional Gaussian mixture model to realize the adaptive adjusting the number of Gaussian distribution,and introduce the illumination change parameters to update the learning-rate according to the variation of the illumination.Image’s background and foreground are segmented by the above method,optimizing the detection results of Gaussian mixture model through the calculation of pixel.Experimental results show that the new method not only can separate the goals effectively and reliably,but also can get better detection effect.

Key words: Gaussian mixture model, target detection, background modeling, Gaussian distribution, background subtraction

摘要： 针对传统的高斯混合模型存在背景建模速度慢、计算复杂度高等问题,提出一种运动目标检测方法。改进传统高斯混合模型的更新过程,实现自适应调整高斯分布个数。引入光照变化参数,根据光照的变化动态更新学习率。利用上述方法得到图像的背景与前景分割,通过像素点的计算来优化高斯混合模型检测结果。实验结果显示,该方法能有效可靠地分离目标,并获得较好的检测效果。

关键词: 高斯混合模型, 目标检测, 背景建模, 高斯分布, 背景差

CLC Number: 

• TP391