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系統識別號 U0002-1609201911570400
中文論文名稱 自適應3D卷積神經網路之異常事件偵測
英文論文名稱 Adaptive Anomaly Detection via 3D Convolutional Neural Network
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士在職專班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 107
學期 2
出版年 108
研究生中文姓名 薛吉全
研究生英文姓名 Chi-Chuan Hsueh
學號 706410106
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2019-07-16
論文頁數 54頁
口試委員 指導教授-顏淑惠
委員-廖弘源
委員-蔡憶佳
中文關鍵字 異常事件偵測  3D卷積神經網路  自適應  級聯式分類器 
英文關鍵字 Anomaly detection  3D convolutional neural network  Spatiotemporal features  Cascade 
學科別分類 學科別應用科學資訊工程
中文摘要 本篇論文提出利用新穎的3D卷積神經網路(3D-CNN, 3D Convolutional Neural Network)學習包含時間及空間的特徵,並將該類神經網路分類器與技術發展純熟的 Cascade架構結合。可以對於不同複雜度的個別區域(如前景與背景),動態學習出一個至數個分類器,級聯成獨立相應的偵測系統。方法上利用將訓練樣本集合細分為典型和非典型兩個子集合,模擬正負樣本;並以增加不同比例高斯雜訊的方式擴充資料量、增加變化性,解決卷積神經網路較難應用於異常事件偵測領域的問題。使用常見的資料集來測試偵測系統之精確度與召回率,由實驗結果顯示我們所提出的方法有不錯的表現,經過後處理後能與其他先進的演算法匹敵。
英文摘要 In this paper we present a neural network (NN) architecture for abnormal events detection in a surveillance system. When training such system, it is a challenge that only normal samples are available for training. In addition, there are various contents inside a surveillance video frame, such like lawn, pedestrian walking area, trees, etc., and each has its own “typical/atypical” pattern. In solving the first problem, we propose a scheme to separate training data into “typical” and “atypical” that serve the roles of negative and positive training samples. In the second problem, we first divide a frame into several blocks. Then, for each block, an adaptive cascaded 3D-CNN classifier is trained. In this way, most of irrelevant blocks (normal) are discarded and only those abnormal or confusing normal blocks are kept for further computations. We evaluate our approach on popular dataset and show that our approach is competitive to state-of-the-art methods.
論文目次 第一章 緒論 1
1.1 研究背景與目的 1
1.2 論文架構 4
第二章 相關文獻回顧 5
第三章 本文方法與網路模型 8
3.1 資料前處理 Data Preprocessing 8
3.2 訓練資料分為典型/非典型樣本 8
I. 經pretrained P3D model 提取特徵 8
II. 由Principal Components Analysis降維 9
III. 計算Mahalanobis Distance 10
IV. 透過Elbow Method分典型/非典型 11
3.3 3D 卷積神經網路(3D Convolutional Neural Network) 12
I. 網路架構 Net 13
II. 空洞卷積 Dilated Convolution 14
III. 損失函數 Loss Function 15
第四章 系統訓練及測試流程 17
4.1 資料擴增 Data Augmentation 17
4.2 訓練 Training 18
I. Stop Criteria for Training a Classifier 18
4.3 測試 Testing 23
4.4 演算法 Algorithm 26
第五章 實驗結果 27
5.1 決定Typical Score Threshold 29
5.2 Typical Accuracy Threshold的必要性 30
5.3 False Negative Rate的必要性 31
5.4 其他相關文獻比較 33
第六章 結論 36
6.1 小結 36
6.2 未來精進方向 36
參考文獻 37
附錄:英文論文 41

圖目錄
圖1異常事件範例 1
圖2級聯分類器示意圖 3
圖3 場景影像作為3D-CNN-Classifier的輸入 8
圖4 馬氏距離計算標準差 10
圖5 Elbow Method應用取分界線 11
圖6 Dilated convolution實例 15
圖7 訓練資料擴增示意圖 17
圖8 非典型訓練樣本增加雜訊後示例 18
圖9 3D-CNN-Classifier訓練架構圖 20
圖10 Adaboost訓練示意圖 21
圖11 異常區域偵測實例 25
圖12 UCSD資料庫 27
圖13 偵測結果實例 28
圖14 區域ROC曲線比較 30
圖15 原始模型與增加準確率門檻值的比較 31
圖16 Original Model 與Consecutive Frame 的ROC比較 34

表目錄
表 1 本篇3D ConvNet詳細結構 14
表 2 以Ped1數據比較加上FNR Threshold條件前後的系統表現 32
表 3 原始系統與兩種後處理方式的Frame-level Confusion Matrix比較 33
表 4 與其他實驗數據比較 35


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