迄今，視覺監控系統已經廣泛地應用於各種場合中，例如公共安全、高樓住宅安檢、家庭即時看護等。隨著生活變得繁忙，越來越多的人們開始獨居生活，因此，為了能夠在意外發生時能夠迅速請求他人協助，家庭的即時看護系統就變得相當重要。然而，傳統的監視系統必須耗費大量的時間與人力去觀察監視畫面的每一片段， 並不符合現今社會的需要，所以，一套基於自動居家看護的視覺監控系統就成了近年來重要的研究課題之一。
但是，異常的行為(例如跌倒、疾病發作…等)往往都是一些稀有而且無法預測的事件，很難事先用特定的統計模型來擬合。為了能夠處理這樣的問題，現存許多文獻的做法是改成幫所有正常的行為建立一個統計模型，一旦輸入的小段影像經過此模型的判定發現機率小於一定閥值，就把此小段影像視為異常發生的片段。基於此，我們事先定義常常重複出現的動作都屬於正常行為的範疇內，而異常行為則不屬於此範疇。再利用上述的方式，為此正常行為的集合建立一個統計模型，用以辨識出所有不屬於此集合的異常行為。
為了建立出較有效率的模型，我們選擇使用條件式隨機場(Conditional Random Field,簡稱CRF)作為統計模型。此模型的好處在於可以在global上統計出所有觀測值序列，狀態間彼此的轉移次數。因此針對所有我們定義正常的行為，只需要建立一個CRF模型，就可依據此來判定新輸入的影片，位於哪些段落上是異常的行為。

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