Robust data fusion in a visual sensor multi-agent architecture

Abstract

A surveillance system that fuses data from several data sources is more robust than those which depends on a single source of input. Fusing the information acquired by a vision system is a difficult task since the system needs to use reliable models for errors and take into account bad performance when taking measurements. In this research, we use a bidimensional object correspondence and tracking method based on the ground plane projection of the blob centroid. We propose a robust method that employs a two phase algorithm which uses a heuristic value and context information to automatically combine each source of information. The fusion process is carried out by a fusion agent in a multi-agent surveillance system. The experimental results on real video sequences have showed the effectiveness and robustness of the system.