Abstract
In video-based surveillance people monitor a wide spatial area through video sensors for anomalous events related to safety and security. The size of the area, the number of video sensors, and the camera's narrow field-of-view make this a challenging cognitive task. Computer vision researchers have developed a wide range of algorithms to recognize patterns in the video stream (intelligent cameras). These advances create a challenge for human supervision of these intelligent surveillance camera networks. This paper presents a new visualization that has been developed and implemented to integrate video-based computer vision algorithms with control of pan-tilt-zoom cameras in a manner that supports the human supervisory role.
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