Abstract
The traditional literature on camera network design focuses on constructing automated algorithms. These require problem-specific input from experts in order to produce their output. The nature of the required input is highly unintuitive, leading to an impractical workflow for human operators. In this work we focus on developing a virtual reality user interface allowing human operators to manually design camera networks in an intuitive manner. From real world practical examples we conclude that the camera networks designed using this interface are highly competitive with, or sometimes even superior to, those generated by automated algorithms, but the associated workflow is more intuitive and simple. The competitiveness of the human-generated camera networks is remarkable because the structure of the optimization problem is a well known combinatorial NP-hard problem. These results indicate that human operators can be used in challenging geometrical combinatorial optimization problems, given an intuitive visualization of the problem.
Highlights
In the camera placement problem, the goal is to find an optimal configuration of cameras to perform some observation task
In this work we focus on developing a virtual reality user interface allowing human operators to manually design camera networks in an intuitive manner
These results indicate that human operators can be used in challenging geometrical combinatorial optimization problems, given an intuitive visualization of the problem
Summary
In the camera placement problem, the goal is to find an optimal configuration of cameras to perform some observation task. In the photogrammetry community a similar problem is studied, where the goal is to select image acquisition locations from which a 3D reconstruction will result in minimal uncertainty over reconstructed points [3,4,5] In this formulation both coverage over environment points and expected measurement quality are important. In general three distinct steps are important in automated camera network design: representation of the problem, formulation of the cost/quality function, and optimization of this cost/quality function
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