| Abstract | Vision systems are increasingly being deployed to perform complexsurveillance tasks. While improved algorithms are being developed to perform
these tasks, it is also important that data suitable for these algorithms be acquired
- a non-trivial task in a dynamic and crowded scene viewed by multiple PTZ
cameras. In this paper, we describe a multi-camera system that collects images
and videos of moving objects in such scenes, subject to task constraints. The system
constructs "task visibility intervals" that contain information about what can
be sensed in future time intervals. Constructing these intervals requires prediction
of future object motion and consideration of several factors such as object
occlusion and camera control parameters. Using a plane-sweep algorithm, these
atomic intervals can be combined to form multi-task intervals, during which a
single camera can collect videos suitable for multiple tasks simultaneously. Although
cameras can then be scheduled based on the constructed intervals, finding
an optimal schedule is a typical NP-hard problem. Due to this, and the lack of
exact future information in a dynamic environment, we propose several methods
for fast camera scheduling that yield solutions within a small constant factor of
optimal. Experimental results illustrate system capabilities for both real and more
complicated simulated scenarios.
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