現今公共場所中已可隨處見到監控攝影機了，而如何從大量視訊資料中發掘出不尋常的行為模式是一個極具挑戰性的問題；然而，由於以電腦視覺演算法來進行物件辨識與意圖偵測仍十分困難，因此發掘異常行為此一任務往往只能藉由人力來完成。近年來，隨著低成本深度攝影機的問世，人體動作擷取的效益與準確度已大為提昇，藉此便可直接提供包含人體關節座標的體感資料。在本研究中，我們規劃了座標轉換、正規化、切割、特徵擷取和降維等完整的系統處理流程，以利後續之異常行為探勘工作；此外，有別於前人研究中大多採用須事先定義 (正常或異常) 行為模式之資料分類技術，本研究規劃將主要以資料分群技術來實現異常行為偵測此一目的，便可不必受限於場景特性而有不同行為模式定義之不一致問題，更明確地而言，我們採用了基於密度的資料分群技術，可透過調整半徑與群內最少點數這兩個參數以妥適地產出異常行為模式。最後，我們採用了兩個資料集來進行實驗評估，結果顯示我們所提出之方法對於異常行為偵測可有顯著的效果。

As there are more and more surveillance cameras installed in public places, a challenging problem is to discover unusual behavior patterns from a huge amount of video data. However, this task is currently only feasible for human beings because both object recognition and intention detection are still difficult for computer vision algorithms. Recently, with the release of low-cost depth cameras, motion data containing 3D coordinates of skeleton joints can be directly captured, thus facilitating following analysis tasks. In this work, we devise a complete system flow which includes steps of coordinate transformation, normalization, segmentation, feature extraction and dimensionality reduction so as to achieve the purpose of discovering unusual behavior patterns. Note that prior works generally require to predefine (either normal or abnormal) behavior patterns and then utilize data classification techniques for further analysis. Instead, we propose in this work to utilize data clustering techniques to discover unusual behavior patterns so that the inconsistencies of defining behavior patterns in various scenarios can be eased. Specifically, we adopt a density-based clustering technique and adjust the values of two parameters (i.e., radius and minimum points) to appropriately generate unusual behavior patterns. Finally, two datasets including MSR action recognition and AffectME (affective multimodal engagement) are used in our experiments for evaluation purposes. Empirical studies show that our approach is effective for discovering unusual behavior patterns.

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