| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 105 === 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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