Implementation of an intelligent, automated target acquisition and tracking systems alleviates the need for operators to monitor video continuously. This system could identify situations that fatigued operators could easily miss.
If an automated acquisition and tracking system plans motions to maximize a coverage metric, how does the
performance of that system change when the user intervenes and manually moves the camera? How can the
operator give input to the system about what is important and understand how that relates to the overall task
balance between surveillance and coverage?
In this paper, we address these issues by introducing a new formulation of the average linear uncovered length
(ALUL) metric, specially designed for use in surveilling urban environments. This metric coordinates the often
competing goals of acquiring new targets and tracking existing targets. In addition, it provides current system
performance feedback to system users in terms of the system's theoretical maximum and minimum performance.
We show the successful integration of the algorithm via simulation.
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