针对自校准地平面的学习监控跟踪模型(英文)

Learning Surveillance Tracking Models for the Self-Calibrated Ground Plane

提出了一种新的多摄像机视觉监控系统的信息融合方法 .信息融合在两个阶段进行 .首先 ,根据相互独立的Cartesian参考坐标系统 (设置在地平面上 ) ,对各个摄像机进行标定 .然后 ,把所有的坐标系变换到一个坐标系统中 .在视觉监控应用中 ,因为摄像机自定标和视觉数据配准技术将使监控设施安置变得更加容易 ,从而可以为公共场合发展更加适用的视觉监控工具 .在解决监控数据的不完整性和不确定性方面 。

We propose a novel method for combining information streamed by a multi-sensor system for visual surveillance. Information fusion occurs in two phases during which all cameras are calibrated with respect to independent global Cartesian reference frames (set on the ground plane) and then all frames are registered into a single coordinate system. The development of automatic calibration and registering of visual data is crucial in visual surveillance applications because it makes easier to install the monitoring infrastructure and, consequently, to develop more accessible Visual Surveillance tools for the public domain. Machine learning techniques are believed to offer the best mathematical tools to handle the uncertainty and incomplete nature of surveillance data.