Maximizing Camera Coverage in Multicamera Surveillance Networks

Abstract

Efficient placement of cameras to perform surveillance tasks has a significant impact on the overall performance as well as cost of video surveillance systems. Identifying an optimal configuration for cameras in a surveillance system to maximize coverage is a combinatorial optimization problem. This paper proposes two algorithms for identifying optimal camera configuration for a multi-camera network with predefined camera locations. We propose an Alternate Global Greedy (AGG) algorithm, a novel variant of the Global Greedy algorithm by making significant modifications to the traditional execution strategy to produce better coverage results. Additionally, we propose an innovative Greedy Grid Voting (GGV) algorithm, which gives preference to cover unique as well as critical regions and effectively addresses a broad range of coverage scenarios mandated by the application. The proposed algorithms are validated on map images across diverse scenarios with predefined camera locations. Experimental results show that our algorithms maximize the coverage results with minimal overlap, compared to the existing greedy techniques.