Continuous camera placement using multiple objective optimisation process

Abstract

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility is inadequate for continuous and automated tracking. In such applications, a sufficient overlap between FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. In addition, most of the existing algorithms employ discrete solution space, which suffers from limited solution accuracy and high computational complexity due to the high dimension of sampled solution space. In this paper, we propose to perform the optimisation of the camera parameters in a continuous solution space. In addition, to incorporate the optimisation of coverage and sufficient overlapped FOVs, a weighted sum approach is utilised to translate a multiple objective optimisation problem into a single objective optimisation process in our previous work. Significantly improved handoff success rates are illustrated via experiments using typical office floor plans in comparison with Erdem and Sclaroff's method. Improved accuracy, enhanced robustness, completeness of the solution set, and reduced computational complexity are accomplished in comparison with our previous algorithms.

Keywords

• computational complexity
• automated tracking
• object tracking
• FOV
• field of view
• object of interest