隨著攝影機架設的普及與影像處理技術的發展，以視覺為基礎的行為分析已成為電腦視覺中即為重要的領域。本論文透過公共開放空間中固定攝影機所截取的監視畫面來分析人的行為，目的在分出該人的動作是屬於違法或是合法行為，在本篇論文中，違法行為包括抽菸與喝飲料行為，而合法行為則囊括講手機與其他行為。相較於過去的論文專著，本論文是在不建立背景的情況下，對上述行為進行分類的動作。本論文大致可分為三個部份，分別是臉部區域的截取，多序列手部樣本點的新增與位置更新，以及特徵截取與最後的行為分析等三個步驟。本系統是透過人臉與手的接觸時間，煙霧偵測的結果，以及手持物體的大小等三個特徵來分類行為，最後以一個決策樹來合併上述三個特徵達到行為分類的目的。經實驗證實，在不同光源強度，不同環境背景，以及不同人的行為習慣下，本論文提出的方法都可以達到不錯的辨識效果。With the popularization of image capturing devices and the mature development of image processing technique, vision-based human behavior analysis gradually attracts the attention and utilized in many fields. In order to detect illegal behaviors, cameras have to be mounted in public space to analyze human behavior to determine whether it is illegal or not. In this thesis, we focus on detecting smoking and drinking in certain public spaces as the illegal behaviors. Comparing with other works, our work does not need to establish the background in advance to classify human behavior. The proposed system consists of three main modules including face region extraction, multiple hand samples extraction, and features extraction and behavior analysis. We extract three features from each human behavior. They are the touching time between the face and hand samples, smoke detection, and handheld object detection. Then a decision tree is employed to classify the human behavior using the extracted three features. Experimental result demonstrate that the proposed method can be suited and successfully applied in many environments under various conditions, such as different illumination intensities, different backgrounds, and the different habits exhibited by human.
