Abstract
Detecting moving objects in video streams is the first relevant step of information extraction in many computer vision applications, e.g. video surveillance systems. In this work, a video segmentation framework by dynamic background modelling is presented. Our approach aims to update suitably the background model of a scene that is recorded by a static camera. For such purpose, we develop an optical flow based methodology to suitable track moving objects, which can stop or change smoothly their movement along the video. Moreover, a light variations identification stage, is employed to avoid possible confusions between illumination changes and objects in movement. Regarding this, our approach is able to ensure a suitable background modelling in real world scenarios. Attained results show that our framework outperforms, in well-known datasets, state of the art methodologies.Keywordsbackground subtractionoptical flowtracking
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