Statistical background subtraction for a mobile observer
Statistical background subtraction for a mobile observer
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DOI：
10.1109/iccv.2003.1238315
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发表时间：
2003-10
期刊：
影响因子：
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通讯作者：
E. Hayman;J. Eklundh
中科院分区：
文献类型：
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作者：
E. Hayman;J. Eklundh
统计背景建模和减法已被证明是一种流行的和有效的一类算法，用于分割独立移动的前景对象从静态背景，而不需要任何先验信息的前景对象的属性。我们提出了两个关于这个主题的贡献，旨在对机器人的积极头部安装在一个移动的车辆。在车辆的车轮不被驱动的时期，相机平移几乎为零，并且背景减除技术是适用的。这也与监控和视频会议高度相关。第一部分提出了一个有效的概率框架，当相机摇摄和倾斜。一个统一的方法是开发用于处理各种来源的错误，包括运动模糊，亚像素相机运动，混合像素在对象边界，以及不确定性，在背景稳定所造成的噪声，未建模的径向失真和小平移的相机。第二个贡献方面的贝叶斯方法，特别是将有关背景是否尚未发现移动前景物体的不确定性。这是系统初始化期间的重要要求。我们不能假设背景模型提前可用，因为这将涉及存储机器人操作环境的每个房间中的每个可能位置的模型。相反，背景模式必须在线生成，很可能存在移动对象。
Statistical background modelling and subtraction has proved to be a popular and effective class of algorithms for segmenting independently moving foreground objects out from a static background, without requiring any a priori information of the properties of foreground objects. We present two contributions on this topic, aimed towards robotics where an active head is mounted on a mobile vehicle. In periods when the vehicle's wheels are not driven, camera translation is virtually zero, and background subtraction techniques are applicable. This is also highly relevant to surveillance and video conferencing. The first part presents an efficient probabilistic framework for when the camera pans and tilts. A unified approach is developed for handling various sources of error, including motion blur, subpixel camera motion, mixed pixels at object boundaries, and also uncertainty in background stabilisation caused by noise, unmodelled radial distortion and small translations of the camera. The second contribution regards a Bayesian approach to specifically incorporate uncertainty concerning whether the background has yet been uncovered by moving foreground objects. This is an important requirement during initialisation of a system. We cannot assume that a background model is available in advance since that would involve storing models for each possible position, in every room, of the robot's operating environment.. Instead the background mode must be generated online, very possibly in the presence of moving objects.