Abstract: This paper presents an improved algorithm of Gaussian mixture model based on inter-frame differencing blocking 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 moving-object detection and simple models are used to reduce the amount of computation. It passes through adaptive learning rate to accelerate the formation and regression of the background. Experimental results show the algorithm can take into account the detection accuracy and computational cost.

Key words: moving-object detection, inter-frame differencing, blocking model, Gaussian mixture model

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。

关键词: 运动目标检测, 帧间差分, 分块模型, 混合高斯模型