Camera Network Coverage Improving by Particle Swarm Optimization

Abstract

This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm optimization (PSO) algorithm which can efficiently find an optimal orientation for each camera. By this optimization the total FOV coverage of the whole camera network is maximized. This new method can also deal with additional constraints, such as a variable region of interest (ROI) and possible occlusions in the ROI. The experiments showed that the proposed method has a much better performance and a wider application scope. It can be effectively applied in the design of any practical camera network.

Optimizing Deployment Cost in Camera-Based Wireless Sensor Networks

“…al. approach in [6] and Yi-Chun Xu et al in [12], we consider a coverage quality parameter and deal with targets instead of area coverage. Here we must determine camera's positions and orientation, while coverage quality can be specified by the user.…”

“…Authors in [12] improved the field of view (FOV) coverage of a camera network. They considered randomly scattered cameras in a wide area, where each camera may adjust only its orientation and not its localisation.…”

Coverage-Enhancing algorithm for video sensor network based on improved particle swarm optimization

“…We adopt node model of video sensor network similar with that in [8]. As illustrated in Figure 1, the position of video sensor node C is ) , ( y x , the angle of field of view is denoted by α 2 , the azimuth of sensing orientation d  is defined as θ , and R is the biggest sensing distance of node.…”

“…In this kind of methods, each camera is regarded as a virtual particle and can be repelled by neighbor cameras, thus the problem of nodes' direction adjustment is converted into the problem of particles' union distribution, and then the coverage of the directional network can be enhanced effectively through decreasing overlapping and blind area. However, when the forces between nodes are in equilibrium, the directions of nodes are not necessarily the most optimum [8]. But because the directions cannot move anymore, sometimes the coverage rate is affected.…”

“…But because the directions cannot move anymore, sometimes the coverage rate is affected. In order to overcome the shortage of potential field-based methods, Xu et al [8] [9] proposed to solve the coverage of video sensor network by using PSO. Since traditional PSO is easy to fall into local optima [10], the mentioned coverage methods of video sensor network based on PSO have similar disadvantages such as local optima and low convergence speed.…”