Complementary background models for the detection of static and moving objects in crowded environments

In this paper we propose the use of complementary background models for the detection of static and moving objects in crowded video sequences. One model is devoted to accurately detect motion, while the other aims to achieve a representation of the empty scene. The differences in foreground detection of the complementary models are used to identify new static regions. A subsequent analysis of the detected regions is used to ascertain if an object was placed in or removed from the scene. Static objects are prevented from being incorporated into the empty scene model. Removed objects are rapidly dropped from both models. In this way, we build a very precise model of the empty scene and improve the foreground segmentation results of a single background model. The system was validated with several public datasets, showing many advantages over state-of-the-art static objects and foreground detectors.

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