Toward Global Sensing Quality Maximization: A Configuration Optimization Scheme for Camera Networks

TL;DR

This paper presents a novel optimization scheme for camera network configurations that maximizes sensing quality for multiple targets, validated through simulations and real experiments on AprilTag detection.

Contribution

It introduces a new metric for sensing quality based on pixel occupation and formulates a global optimization approach for camera configuration.

Findings

Improved AprilTag detection performance.

Effective global reconfiguration strategy.

Open-source implementation available.

Abstract

The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized globally and simultaneously. We first propose to use the number of pixels occupied by a unit-length object in image as a metric of the sensing quality of the object, which is determined by the parameters of the camera, such as intrinsic, extrinsic, and distortional coefficients. Then, we form a single quantity that measures the sensing quality of the targets by the camera network. This quantity further serves as the objective function of our optimization problem to obtain the optimal camera configuration. We verify the effectiveness of our approach through extensive simulations and experiments, and the results reveal its improved performance on the AprilTag detection tasks. Codes and related utilities for this work are open-sourced and available at https://github.com/sszxc/MultiCam-Simulation.