A Generic and Scalable Approach to Maximize Coverage in Diverse Indoor and Outdoor Multicamera Surveillance Scenarios

Abstract

Efficient deployment of cameras optimizes the costs and benefits in a multicamera surveillance network. Most camera placement algorithms are developed for surveillance regions with predefined camera locations, where optimal orientation for every given camera location is determined to maximize coverage. The existing approaches to optimizing the number of cameras, locations, and orientations focus on specific scenarios and are neither generic nor scalable across diverse surveillance scenarios. This article proposes a generic and scalable reward penalty score (RPS) algorithm for optimal camera placement in diverse indoor and outdoor scenarios to efficiently attain a specified coverage requirement. The camera placement decision is driven by maximizing total coverage over the surveillance region with a minimum number of cameras at optimal locations and orientations. Based on the RPS algorithm, we extend the existing Greedy grid voting (GGV) algorithm that was limited to optimizing camera orientations for predefined locations. The extended GGV (EGGV) algorithm is broader in scope and optimizes the number of cameras, their locations, and orientations. Overall, the RPS algorithm produces superior coverage results over indoor and outdoor scenarios compared to the existing camera placement algorithms on map images of diverse surveillance scenarios ranging from small to large.