Coverage Optimization of Camera Network for Continuous Deformable Object
This work addresses a domain-specific problem in computer vision for surveillance or monitoring applications, presenting an incremental improvement in camera deployment methods.
The paper tackles the problem of optimizing camera network deployment for continuous visual coverage of deformable objects by discretizing the object contour and selecting feature points to represent deformation trajectories, proposing an improved wolf pack algorithm for optimization, with simulation results demonstrating effectiveness.
In this paper, a deformable object is considered for cameras deployment with the aim of visual coverage. The object contour is discretized into sampled points as meshes, and the deformation is represented as continuous trajectories for the sampled points. To reduce the computational complexity, some feature points are carefully selected representing the continuous deformation process, and the visual coverage for the deformable object is transferred to cover the specific feature points. In particular, the vertexes of a rectangle that can contain the entire deformation trajectory of every sampled point on the object contour are chosen as the feature points. An improved wolf pack algorithm is then proposed to solve the optimization problem. Finally, simulation results are given to demonstrate the effectiveness of the proposed deployment method of camera network.