摘要
针对混合高斯背景模型计算量过大、对复杂场景的适应能力较差等问题,提出了一种基于帧差分块和自适应学习率的混合高斯背景模型改进算法。引入分块模型思想,有效结合了像素的空域信息;根据帧间差分结果,判断可疑前景区域和背景区域,提高了检测灵敏度;针对前景可疑区域采用复杂模型,保证运动目标检测的精度,反之采用简单模型降低计算量;通过自适应学习率,加速背景的形成与消退。实验结果证明该算法较好地兼顾了检测精度和计算代价。
This paper presents an improved algorithm of Gaussian mixture model based on inter-frame differencing block-ing model and adaptive learning rate for the problem of too large calculation, poor ability to adapt to the complex scenes and other issues. It introduces the blocking model, effectively integrates information of pixel airspace, based on the inter-frame difference results, it determines the suspicious foreground region and background region to improve the detection sensitivity. Complex models are used for suspicious areas to ensure the accuracy of the movingobject detection and simple models are used to reduce the amount of computation. It passes through adaptive learning rate to accelerate the for-mation and regression of the background. Experimental results show the algorithm can take into account the detection accuracy and computational cost.
出处
《计算机工程与应用》
CSCD
2014年第23期176-180,共5页
Computer Engineering and Applications
关键词
运动目标检测
帧间差分
分块模型
混合高斯模型
moving-object detection
inter-frame differencing
blocking model
Gaussian mixture model
