Abstract:

The problem of generative process tracking involves detecting and adapting to changes in the underlying generative process that creates a time series of observations. It has been widely used for visual background modelling to adaptively track the generative process that generates the pixel intensities. In this paper, we extend this idea to audio background modelling and show its applications in surveillance domain. We adaptively learn the parameters of the generative audio background process and detect foreground events. We have tested the effectiveness of the proposed algorithms using synthetic time series data and show its performance on elevator audio surveillance.