Optimal Camera Placement for Automated Surveillance Tasks |
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Authors: | Robert Bodor Andrew Drenner Paul Schrater Nikolaos Papanikolopoulos |
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Affiliation: | (1) Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA |
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Abstract: | Camera placement has an enormous impact on the performance of vision systems, but the best placement to maximize performance
depends on the purpose of the system. As a result, this paper focuses largely on the problem of task-specific camera placement.
We propose a new camera placement method that optimizes views to provide the highest resolution images of objects and motions
in the scene that are critical for the performance of some specified task (e.g. motion recognition, visual metrology, part
identification, etc.). A general analytical formulation of the observation problem is developed in terms of motion statistics
of a scene and resolution of observed actions resulting in an aggregate observability measure. The goal of this system is
to optimize across multiple cameras the aggregate observability of the set of actions performed in a defined area. The method
considers dynamic and unpredictable environments, where the subject of interest changes in time. It does not attempt to measure
or reconstruct surfaces or objects, and does not use an internal model of the subjects for reference. As a result, this method
differs significantly in its core formulation from camera placement solutions applied to problems such as inspection, reconstruction
or the Art Gallery class of problems. We present tests of the system’s optimized camera placement solutions using real-world
data in both indoor and outdoor situations and robot-based experimentation using an all terrain robot vehicle-Jr robot in
an indoor setting. |
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Keywords: | Camera networks Robot/camera placement Observability Optimization Sensor networks Vision-based robotics |
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