光線適應性背景模型於前景主體抽取

Abstract

利用串流影像資訊於前景人物/物體的擷取能在許多地方應用，如:人機介面、安全監控、居家安全照護等系統。本論文中將提出一個可以針對光線改變的適應性背景模型於前景主體抽取的系統，在一般光線亮度改變差別不大時，可以簡單的使用HSV色彩模型或利用GMM 於RGB 色彩模型將前後景分離；但當背景光線突然有較大的改變時，我們無法使用上述二者系統將完整的前景資訊分離，因此我們使用適應性的背景模型，克服背景光線的突然改變，達到前、後景的分離。且當人物在原處停留一段時間時，我們亦使用適應性的歷史資料背景模型判斷人物是靜止或移動。 在本論文中，為了能快速適應亮度變化，我們必須先偵測光線的改變，在另ㄧ方面，有關人物行為的改變，如：移動人物的停止、靜止人物的開始移動，也都包含在本論文中。最後針對我們提出的適應性背景模型進行實驗驗證，當背景光線改變時，此模型皆能有效的將前景主體抽取出。

Foreground subject/object extraction from video streams has a wide range of application such as human-machine nterface, security surveillance, home care system, etc.The objective of this thesis is to provide an adaptive background odeling for foreground extraction to count for lighting and subject moving change. With a little lighting change, the foreground subject/object can be extracted easily in the HSV color space, on our proposed or in the RGB color space, by a Gaussian Mixture Model. With a large lighting change, we cannot extract the foreground image completely both systems above. To solve this, we propose an adaptive background modeling framework. When a moving subject stops for a while, we also use the History-Based Background Adaptation to classify the pixels whether it is to stop of moving subject or not. In this framework, a lighting change detection scheme facilitates the quick adaptation to the intensity change. On the other hand, a subject action change, for example, from moving to a stop or from a stop to moving, is also detected and adapted to our framework. Our proposed adaptive background modeling demonstrates consistently excellent foreground extraction despite the various changes encountered.