Region level segmentation based on a derivative approach for video tracking process

In this paper a new video tracking process is proposed. Our approach uses two description levels for the segmentation mechanism. The first one, the pixel level, performs the extraction of every point in movement, forming, as result, connected regions. For this stage, a new adaptive reference image (ARI) algorithm is described, based on a derivative approach. This choice presents a robust method facing changes in illumination. The second level, the region level, is driven by the contents of a model set. This high level object description, defined by geometric attributes and a motion model lets us associate the ARI process with the handle objects. This stage sets this up as a Bayesian model formulation. The identification and updating process is done by a modified expectation-maximization (EM) algorithm. This step establishes the relationship between regions (the ARI process output) and the object models (OM). For each correspondence, the EM procedure is started, updating every attribute of the object description. This framework defines a complete unsupervised tracking procedure, robust regarding occlusions, over- or sub-segmentations and background brightness variations. Several real traffic examples are included at the end of the paper.