Abstract: For the traditional Gaussian mixture model cannot detect complete moving object and is prone to detect the background as the foreground region, an improved algorithm was proposed for moving object detection based on Gauss mixture model. The Gaussian background model mixed with improved frame difference method for integration, distinguished the uncovered background area and moving object region, which could extract the complete moving object. To give a larger background updating rate of uncovered background area, the background exposure of regional influences was eliminated. In complex scene, it used the method of replacement by background model to improve the stability of the algorithm. The experiments prove that the improved algorithm has been greatly improved in the aspects of adaptability, accuracy, real-time, practicality and so on, and can correctly and effectively detect moving object in the situation with various complicated factors.

Key words: Gaussian mixture model, moving object detection, frame difference method, uncovered background area, background update rate

摘要： 针对传统的混合高斯模型存在无法完整检测运动目标、易将背景显露区检测为前景等问题，提出了一种基于混合高斯模型的运动目标检测的改进算法。通过将混合高斯模型与改进帧差法进行融合，快速区分出背景显露区和运动目标区，从而提取出完整的运动目标。在运动目标由静止缓慢转为运动的情况下，为背景显露区给予较大背景更新速率，消除了背景显露区对运动目标检测的影响。在兼顾混合高斯模型在复杂场景中对噪声处理效果差的基础上，利用背景模型替换的方法来提高算法的稳定性。经过反复实验，结果表明改进后的算法在自适应性、正确率、实时性、实用性等方面有了很大的改进，能够在各种复杂因素存在的情况下正确有效地对运动目标进行检测。

关键词: 混合高斯模型, 运动目标检测, 帧差法, 背景显露区, 背景更新速率