Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling

In this paper we present a foreground segmentation and tracking system for monocular static camera sequences and indoor scenarios that achieves correct foreground detection also in those complicated scenes where similarity between foreground and background colours appears. The work flow of the system is based on three main steps: An initial foreground detection performs a simple segmentation via Gaussian pixel color modeling and shadows removal. Next, a tracking step uses the foreground segmentation for identifying the objects, and tracks them using a modified mean shift algorithm. At the end, an enhanced foreground segmentation step is formulated into a Bayesian framework. For this aim, foreground and shadow candidates are used to construct probabilistic foreground and shadow models. The Bayesian framework combines a pixel-wise color background model with spatial-color models for the foreground and shadows. The final classification is performed using the graph-cut algorithm. The tracking step allows a correct updating of the probabilistic models, achieving a foreground segmentation that reduces the false negative and false positive detections, and obtaining a robust segmentation and tracking of each object of the scene.

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► Combination of pixel-wise and region based segmentation with objects tracking.
► Tracking is performed with mean shift algorithm using foreground information.
► Foreground detection within a MAP-MRF framework.
► Spatial-color Gaussian Mixture foreground and shadow models are used for each object.