Efficient Mathematical Programming Formulation and Algorithmic Framework for Optimal Camera Placement

TL;DR

This paper introduces a novel integer programming framework with adaptive sampling strategies for optimal camera placement, significantly improving coverage efficiency and providing theoretical bounds and practical insights.

Contribution

It proposes a new mathematical programming formulation combined with adaptive sampling methods, E&E and TUS, to enhance camera coverage solutions efficiently.

Findings

E&E improves coverage by 3.3-16.0% over random sampling.

TUS achieves 6.9-9.1% gains in open environments with tight budgets.

Approach reduces sampling budget by 30-70% while maintaining coverage.

Abstract

Optimal camera placement plays a crucial role in applications such as surveillance, environmental monitoring, and infrastructure inspection. Even highly abstracted versions of this problem are NP-hard due to the high-dimensional continuous domain of camera configurations (i.e., positions and orientations) and difficulties in efficiently and accurately calculating camera coverage. In this paper, we present a novel framework for optimal camera placement that uses integer programming and adaptive sampling strategies to maximize coverage, given a limited camera budget. We develop a modified maximum k-coverage formulation and two adaptive sampling strategies, Explore and Exploit (E&E) and Target Uncovered Spaces (TUS), that iteratively add new camera configurations to the candidate set in order to improve the solution. E&E focuses on local search around camera configurations chosen in previous iterations, whereas TUS focuses specifically on covering regions that were previously uncovered. We first conduct theoretical analysis to provide bounds on the probability of finding an optimal solution and expected sampling needs, while ensuring monotonic improvements in coverage. Then, we conduct a detailed numerical analysis over different environments. Results show that E&E achieves coverage improvements of 3.3-16.0% over all baseline random sampling approaches, while maintaining manageable computational times. Meanwhile, TUS performs well in open environments and with tight camera budgets, achieving gains of 6.9-9.1% in such conditions. Compared to the baseline, our approach achieves similar coverage using only 30-70% of the sampling budget, demonstrating its computational efficiency. Through a case study, we obtain insights into optimal camera placement decisions for a typical indoor surveillance application.