复杂场景实时目标检测方法

针对含有非平稳背景的复杂场景,提出一种基于空间混合高斯模型的实时目标检测方法.该方法以混合高斯模型作为颜色分布的统计框架,并在空间邻域中利用背景与前景隶属度之间的竞争确定像素的归属,提高了检测准确率；同时通过基于偏差均值的匹配判断和低权重模型的移除提升了混合高斯统计框架的性能,以实时地对运动目标进行准确的检测.实验结果表明,文中方法对非平稳背景有很好的适应能力,在检测准确率和运行效率上均优于其他检测方法.

Real-time Objects Detection Method in Complex Scenes

For better object detection in scenes with nonstationary background,a novel real-time method based on spatial Gaussian mixture model is proposed.Gaussian mixture model is adopted to estimate the color distribution and whether a pixel belongs to background or foreground.In this way,the detection accuracy can be improved.Meanwhile,some modifications of the Gaussian mixture statistical framework are conducted,including model matching through mean of deviation and removal of models with low weight,which make it capable of detecting moving object accurately in real time.Experimental results have demonstrated the adaptability to nonstationary backgrounds,and the superiority in accuracy rate and efficiency over other detection methods.