Computer and Modernization
焦玉清;王文中;罗斌
【期刊名称】《计算机与现代化》 【年(卷),期】2016(000)011
【摘要】在摄像机固定的视频监控中，动态背景下的运动目标检测是一个非常有挑战的基础问题。本文提出一种鲁棒的运动目标检测方法。首先，为有效利用场景区域的先验信息，把事先定义的语义区域信息融合到ViBe算法中，消除一些特定语义区域中的动态背景干扰。其次，根据改进的ViBe算法的结果估计背景和前景的全局外观GMM模型，利用该模型对每个像素进行进一步的分类，从而通过全局外观模型去除一些错误的检测结果。最后，使用超像素对结果进行后期处理，得到更加精确的检测结果。实验结果表明，本文方法在检测有强烈动态背景干扰的监控视频时，远远超过了其他的运动目标检测方法。%Moving object detection in dynamic background is a very challenging fundamental problem in video surveillance. This paper presents a robust moving object detection method. First, we develop an effective ViBe algorithm against dynamic back-ground by incorporating the scene prior information that is predefined in initial frame. Then, the global GMM models of fore-ground objects and background are estimated by foreground and background pixels detected by the improved ViBe algorithm. These GMM models are employed to classify every pixel effectively and remove some of the false results. For further alleviating the effects of noises, the superpixel-based refinement is
Computer and Modernization
