Solving Optimal Camera Placement Problems in IoT Using LH-RPSO
Solving Optimal Camera Placement Problems in IoT Using LH-RPSO
复制标题
使用 LH-RPSO 解决物联网中的最佳相机放置问题
DOI：
10.1109/access.2019.2941069
复制
发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Gu, Haoran
中科院分区：
文献类型：
--
作者：
Wang, Xiaohui;Zhang, Hao;Gu, Haoran
随着公共安全和智能生活需求的日益增长以及物联网的发展，视觉传感器网络的结构和应用越来越复杂。它不再是一个简单静态监控的系统，而是一个可以进行智能处理的复杂系统，如目标定位、识别、跟踪等，为了高效地完成各种任务，提前确定摄像机网络的部署计划至关重要。许多研究将摄像机最优布局问题离散化为一个NP难的二进制整数规划（Binary Integer Programming，BIP）问题，并提出了贪婪算法、半定规划、模拟退火等近似求解方法。为了得到更精确的结果，我们不再对连续摄像机参数进行离散化，而是直接在连续域中处理连续值。同时，提出了一种基于拉丁超立方体的递归粒子群优化算法（LH-RPSO），有效地解决了该问题。为了验证该算法的有效性，我们将其与标准粒子群优化算法（PSO）和递归粒子群优化算法（RPSO）进行了比较。室外平面区域的仿真结果说明了该算法的有效性。
With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex. It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on. In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance. Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semi-definite programming, simulated annealing, etc. In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type). To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly. Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem. To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO). Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.