為了徹底防範公共以及私人場所的安全，監視攝影機被廣泛布置在各個角落以防止竊盜、非法活動、或其他可能威脅大眾安全的事件。而分析這些監視影片的工具用於從評價人類的行為模式來識別潛在的安全風險顯得非常的必須且重要。在這篇論文中，我們把重點放在同時追蹤多人的行蹤並且分析兩兩間的互動關係，利用這些關係來了解他們在做何種活動。有別於一般的根據圖片的方法，我們提出了一種新的方法有效的在近距離的監視影片識別了活動。關鍵的動態特徵以及運動方向從身體的幾個特定的部分(四肢、軀幹)提取出來。像是推人、打人、踢人這些活動就跟關鍵的動態特徵非常相關。我們給定了一些規範來識別這先關鍵的動態特徵。在把所有的人類都標記好了動態特徵後，我們就可以利用簡單的規範來判定任兩人在做何種互動。在每張圖片都得到辨識互動的結果後，利用固定時間區間內的密度來判斷此時間區間有沒有此活動發生。最後再利用SVM的模型把結果近一步的優化。

The surveillance cameras have been largely deployed to record and track moving objects to prevent theft, illegal activities or whatever so as to secure the safety in public or private places prevalently. Tools for analyzing those surveillance videos should thereby be necessary and important for evaluating the behavioral patterns of humans identified as potential security risk. In this thesis, we focus on tracking multiple people simultaneously when they are interacting with each other, and then recognizing their activities as well. A novel motion-based approach, rather than the image-based, to effectively recognize the moving activities is proposed for the near-field visual surveillance. Key actions are extracted and characterized by motion directions of several portions (limbs, torso) of human body. Some activities, such as pushing, punching, kicking, etc. are strongly relative to these key actions associated with blobs of human limbs. We give some rules for identifying these key actions. After key actions on identified human blobs for two-person interactions labeled, we can recognize what activity they are doing by means of simple principle: The activity with the highest density in a fixed-time period will be the result. Finally, a SVM–based training procedure is employed to further refine the decision.

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