Scene recognition with audio-visual sensor fusion

Several surveillance applications are characterized by the ability to gather information about the scene from more than one sensor modality, and heterogeneous sensor data must then be fused by the decision-maker. In this paper, we discuss the issues relevant to developing a model for fusion of information from audio and visual sensors, and present a framework to enhance decision-making capabilities. In particular, our methodology focuses on the issues of temporal reasoning, uncertainty representations, and coupling between features inferred from data streams coming from different sensors. We propose a conditional probability-based representation for uncertainty, along with fuzzy rules to assist decision-making, and a matrix representation of the coupling between sensor data streams. We also develop a fusion algorithm that utilizes these representations.