基于自适应背景模型的全方位视觉人体运动检测

设计了一种新的全方位视觉系统，用来在室内对多个人体目标进行实时运动检测。系统中使用全方位摄像机作为图像采集设备，能在一幅图像中获取水平方向360°的环境信息。在检测开始之前，首先由摄像机对无人环境持续观测一段时间，建立背景的统计模型；在检测开始之后，将每一时刻全方位摄像机采集到的图像变换成柱状全景图像，再利用背景的统计模型，通过自适应的动态背景减除算法得到前景区域，同时在线更新背景模型。最后进行区域分割，确定人体的位置。试验结果表明，该系统在复杂背景的室内环境下，有较好的实时性和检测效果。

Motion Detection for Human Bodies Basing Adaptive Background Subtraction by Using an Omnidirectional Camera

A new real-time omnidirectional vision system is proposed for tracking multiple targets in indoor environments. In this system, an omnidirectional camera is used to obtain the 360°view images of the global scene. First, it builds the background model by observing the scene without people in it. When the detecting process starts, omnidirectional images are changed into the cylindrical panoramic images, and an adaptive background subtraction method is utilized to segment the moving regions and locate the positions of human bodies. Thus, multiple human bodies can be tracked. Experimental results show that the proposed system performs well in indoor, complex environments.