Coverage is a basic and critical issue for design and deployment of visual sensor networks, however, the optimization problem is very challenging especially when considering coverage of three-dimensional (3-D) scenarios. This paper provides a brief survey of mainstream coverage optimization methods for visual sensor networks, including the greedy algorithm, genetic algorithm (GA), particle swarm optimization (PSO), binary integer programming (BIP) and differential evolution algorithm (DE). We provide an efficient open-source C++ implementation of these algorithms and compare their performance on a typical camera deployment problem for coverage of 3-D objects. In order to improve the computational efficiency, a parallel visual occlusion detection approach is proposed and implemented with graphic processing units (GPUs), which are then integrated into all of the aforementioned optimization approaches for a fair comparison. Evaluation results show that (1) the proposed parallel occlusion detection algorithm largely improves the computational efficiency; (2) among the five typical approaches, BIP has the best coverage performance yet with the highest time cost, and greedy algorithm is the fastest approach at the price of coverage performance; GA, PSO, and DE achieve a compromise between the performance and the time cost, while DE has better coverage performance and less time cost than PSO and GA. These results could serve as engineering guidelines and baselines for further improvement of coverage optimization algorithms.

中文翻译：

---

用于观察3D对象的视觉传感器网络的覆盖范围优化：调查和比较

覆盖范围是视觉传感器网络设计和部署的基本和关键问题，但是，优化问题非常具有挑战性，尤其是在考虑三维（3-D）场景的覆盖范围时。本文简要概述了视觉传感器网络的主流覆盖优化方法，包括贪婪算法，遗传算法（GA），粒子群优化（PSO），二进制整数规划（BIP）和差分进化算法（DE）。我们提供了这些算法的高效开源C ++实现，并比较了它们在3D对象覆盖的典型摄像机部署问题上的性能。为了提高计算效率，提出了一种并行视觉遮挡检测方法，并通过图形处理单元（GPU）来实现，然后将其集成到所有上述优化方法中，以进行公平比较。评估结果表明：（1）提出的并行遮挡检测算法大大提高了计算效率；（2）在五种典型方法中，BIP具有最佳的覆盖性能，却具有最高的时间成本，而贪婪算法是以覆盖性能为代价的最快方法；GA，PSO和DE在性能和时间成本之间取得了折衷，而DE与PSO和GA相比，具有更好的覆盖性能和更少的时间成本。这些结果可以作为工程指导和基线，以进一步改善覆盖率优化算法。评估结果表明：（1）提出的并行遮挡检测算法大大提高了计算效率；（2）在五种典型方法中，BIP具有最佳的覆盖性能，但时间成本最高，而贪婪算法是以覆盖性能为代价的最快方法；GA，PSO和DE在性能和时间成本之间取得了折衷，而DE与PSO和GA相比，具有更好的覆盖性能和更少的时间成本。这些结果可以作为工程指导和基线，以进一步改善覆盖率优化算法。评估结果表明：（1）提出的并行遮挡检测算法大大提高了计算效率；（2）在五种典型方法中，BIP具有最佳的覆盖性能，但时间成本最高，而贪婪算法是以覆盖性能为代价的最快方法；GA，PSO和DE在性能和时间成本之间取得了折衷，而DE与PSO和GA相比，具有更好的覆盖性能和更少的时间成本。这些结果可以作为工程指导和基线，以进一步改善覆盖率优化算法。GA，PSO和DE在性能和时间成本之间取得了折衷，而DE与PSO和GA相比，具有更好的覆盖性能和更少的时间成本。这些结果可以作为工程指导和基线，以进一步改善覆盖率优化算法。GA，PSO和DE在性能和时间成本之间取得了折衷，而DE与PSO和GA相比，具有更好的覆盖性能和更少的时间成本。这些结果可以作为工程指导和基线，以进一步改善覆盖率优化算法。