在本論文中，我們提出了一個以Principal Component Analysis (PCA)為基礎，新的異常檢測方法，將網路流量異常診斷的方法，應用在影像異常偵測上，我們首先經過時間空間特徵點的偵測Space-Time Interest Points(STIPs)，得到影片中空間時間響應值較大的點，並在這些有興趣的點周圍取特徵，形成一個小立方體(cube)，再將畫面沿著縱軸及橫軸做若干條線的切割，影片將會形成獨立的長方體，並且訓練各自的模型，我們使用了幾種空間時間的特徵來描述小立方體: 方向梯度直方圖(histogram of oriented gradient, HOG)，光流方向直方圖(histogram of oriented optical flow, HOF)、方向描述子(motion direction descriptor)和速度描述子(motion magnitude descriptor)，這樣不但包含了速度及方向上的資訊也包含了小立方體上外觀的特徵，我們在決定模型主成份與剩餘主成份的方法是利用主成份分析的結果，將每個特徵都視為一個資料點，計算出資料點與模型間的距離，與正常門檻值相比後判斷目前的資料點是否為異常。因為只運用到少量且特定的變異量做偵測，能夠有效降低維度。
我們也將提出的演算法與一些公開的資料集做比較，透過模擬實驗以驗證我們提出的想法及流程的可靠性及準確性。

In this thesis, we propose a new method for abnormal detection based on the Principal Component Analysis (PCA) and apply network traffic anomaly diagnosis to the detection of image anomalies. First, we obtained some relatively high points of response in the video by detecting the space-time interest points (STIPs), then gathered information around the points to form a cube, and finally segmented the picture with horizontal and vertical lines into partial windows, which divided the video into cuboids training separate models. We used several spatial and temporal features to describe the cuboids: histogram of oriented gradient (HOG), histogram of oriented optical flow (HOF), motion direction descriptor, and motion magnitude descriptor. These provided not only information in velocity and directionality, but also physical features of the cuboids. In deciding the model principles and residual principles, we resorted to the Principal Component Analysis and counted each individual feature as a data point, thereby calculating the distance between data point and model and judging whether the present data point is abnormal by comparing the distance value with the normal threshold. Because we used only a few specific variances for detection, we were able to reduce their dimension. We also compared our proposed method of calculation with some published datasets, and verified our validity, reliability and accuracy through simulation experiments.

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