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Foreground detection is one of the major tasks in the field of Computer Vision whose aim is to detect changes in image sequences. Many applications do not need to know everything about the evolution of movement in a video sequence, but only require the information of changes in the scene. Detecting foreground to separate these changes taking place in the foreground of the background. It is a set of techniques that typically analyze the video sequences in real time and are recorded with a stationary camera. ==Description== All detection techniques are based on the first model before the entire background of the image. That is, set the background and then see what changes occur in the background. Define it can be very difficult when it contains shapes, shadows and moving objects. In defining the background is assumed that this'' stationary objects' that can be'' 'variations in color and intensity versus time. Scenarios where these apply tèniques tend to be very diverse. Can be highly variable sequences, images with very different lighting, interiors, exteriors, quality or less and up to a large number of possibilities. You need a system that, in addition to process in real time, be able to adapt to these changes. A very good foreground detection system should be able to: * Get the background (estimate) whether it is as if a static variable. * Be robust to lighting changes, repetitive movements at the bottom (leaves, waves, shadows), long-term changes. (A car comes and parks). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Foreground detection」の詳細全文を読む スポンサード リンク
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