Video Segmentation Framework by Dynamic Background Modelling
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 subtraction optical flow tracking
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