结合阴影抑制的混合高斯模型改进算法

混合高斯模型背景法作为运动目标检测的一种经典方法,已经广泛应用于智能视频监控系统中。但是,传统的混合高斯模型背景法容易将阴影误检测为运动目标的一部分。因此,针对该方法在区分阴影和运动目标方面的不足,提出了一种将混合高斯模型背景法和HSV空间阴影抑制相结合的运动目标检测算法。这种改进算法首先将颜色空间转换到HSV空间,初步提取运动目标,然后再利用阴影的灰度值比背景中的灰度值小,而前景的灰度值比背景中灰度值大的特性,检测出运动目标中的阴影。实验结果表明,这种改进的算法明显提高了检测效果,有效抑制了阴影对运动目标检测的干扰,算法实时性也较好。

An improved algorithm of Gaussian mixture model combined with shadow suppression

As a classical method in moving target detection, the background subtraction of the Gaussian mixture model has been widely applied in intelligent video surveillance system. However, this classical method easily recognizes shadows as a part of a moving target. So in this paper, we present a moving target detection algorithm combining the Gaussian mixture model and the shadow suppression in HSV space to overcome the shortage in distinguishing shadows from a moving target. First, we use the three frames subtraction method to detect the changed area in the image. Then we transform RGB space into HSV space to detect moving targets preliminarily. Finally, we detect shadows from the moving target through the features that the gray value of shadows is smaller than that of background and the gray value of the foreground is larger than that of the background. Experimental results suggest that the improved algorithm can obviously improve the detection effect by suppressing the interference of shadows of moving targets and also has good real time performance.