Multilevel Background Initialization using Hidden Markov Models

Most of the automated video-surveillance applications are based on the process of background modelling, aimed at discriminating motion patterns of interest at pixel, region or frame level in a nearly static scene. The issues characterizing an ordinary background modelling process are typically three: the background model representation, the initialization, and the adaptation. This paper proposes a novel initialization algorithm, able to bootstrap an integrated pixel and region-based background modelling algorithm. The input is an uncontrolled video sequence in which moving objects are present, the output is a pixel- and region-level statistical background model describing the static information of a scene. At the pixel level, multiple hypotheses of the background values are generated by modelling the intensity of each pixel with a Hidden Markov Model (HMM), also capturing the sequentiality of the different color (or gray-level) intensities. At the region level, the resulting HMMs are clustered with a novel similarity measure, able to remove moving objects from a sequence, and obtaining a segmented image of the observed scene, in which each region is characterized by a similar spatio-temporal evolution. Experimental trials on synthetic and real sequences have shown the effectiveness of the proposed approach.