A fast approach for omnidirectional surveillance with multiple virtual perspective views

In recent years, video surveillance combined with computer vision algorithms like object detection, tracking or automated behaviour analysis has become an important research topic. However, most of these systems are depending on either fixed or remotely controlled narrow angle cameras. When using the former, the area of coverage is extremely limited, while utilizing the latter leads to high failure rates and troubles in camera calibration. In this paper, a method of extracting multiple perspective views from a single omnidirectional image for realtime environments is proposed. An example application of a ceiling-mounted camera setup is used to show the functional principle. Furthermore a performance improvement strategy is both presented and evaluated.