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Most surveillance and monitoring systems nowadays utilize multiple types of sensors. However, due to the asynchrony among and diversity of sensors, information assimilation-how to combine the information obtained from asynchronous and multifarious sources is an important and challenging research problem. In this paper, we propose a hierarchical probabilistic method for information assimilation in order to detect events of interest in a surveillance and monitoring environment. The proposed method adopts a bottom-up approach and performs assimilation of information at three different levels- media-stream level, atomic-event level and compound-event level. To detect an event, our method uses not only the current media streams but it also utilizes their two important properties- first, accumulated past history of whether they have been providing the concurring or contradictory evidences, and- second, the system designer’s confidence in them. A compound event, which comprises of two or more atomic-events, is detected by first estimating probabilistic decisions for the atomic-events based on individual streams, and then by aligning these decisions along a timeline and hierarchically assimilating them. The experimental results show the utility of our method.
Atrey et al. (Fri,) studied this question.