臺灣博碩士論文加值系統

本論文中，我們將分析視訊影像的異常行為偵測。為了簡化我們的問題，我們將問題視為外圍偵測。而在我們測試的資料中，訓練資料皆為正常行為，而測試資料中則是包含了正常和異常的行為。利用訓練資料建立模型，我們可以定義異常行為在模型中正常機率低於閘值。

基於這個概念，我們使用Kullback–Leibler importance estimation procedure (KLIEP)來計算訓練資料和測試資料的比值做為內圍分數。因為在傳統的做法中分別估測兩者機率密度分布的難度很高，所以我們使用KLIEP透過核函式直接模擬兩者之間的比值，可比避免掉分別估測訓練和測試資料的分布。

而後我們利用訓練資料和一些測試資料來建立模型。根據模型，其他的測試資料也可以得到內圍分數，其中內圍分數代表的就是訓練資料和測試資料的相似程度。透過以內圍為基礎的外圍偵測器和內圍分數，我們可以求出適當的閘值，並結合內圍分數來判斷此局部的內圍分數是不是有過低的情形，藉此決定是否在此局部有無異常發生。

在我們的評估中，我們使用PASCAL方法和ground truth來評估局部比值。經過電腦模擬後，我們發現在UCSD的資料和其他相關文獻比較我們的局部比值有著較高的準確性

In this theme, we consider the analysis of abnormally behavior in surveillance system. To simplify the problem, we formalized it as an outlier detection problem. In our case, all behaviors in training data are normal. By creating a model by training data, we can define abnormalities whose probability is below a certain threshold under this model.

Based on this, we use Kullback–Leibler importance estimation procedure (KLIEP) to compute the ratio of training data and testing data which we used as our inlier score. The KLIEP is a method to estimate the inlier score, not the probability densities themselves. This formulation allows us to avoid non-parametric density estimation, which is known to be a difficult task.

After computing inlier score by KLIEP, we create a model by training data and testing data. According to this model, other testing data can also get a inlier score which can represent the degree of similarity between training data and testing data. Based on the concept of inlier-based outlier detection and normal score, we can determine an appropriate threshold and find which location at testing data is abnormal.

In our evaluation we used PASCAL metric to evaluate our localization rate by ground truth. Through computer simulations, we find that our method has high accuracy of localization rate in UCSD dataset compared with previous works.

第一章序論
1.1 引言 . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 研究動機 . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 本論文之貢獻 . . . . . . . . . . . . . . . . . . . . . 4
1.4 論文章結概述. . . . . . . . . . . . . . . . . . . . . 5

第二章背景回顧
2.1 時間空間異常. . . . . . . . . . . . . . . . . . . . . 6
2.2 特徵. . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 梯度直方圖. . . . . . . . . . . . . . . . . . 9
2.2.2 光流. . . . . . . . . . . . . . . . . . . . . . 11
2.3 機器學習 . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 經由直接比率估測以內圍基礎的異常偵測器 . . . . 15
2.5 結語 . . . . . . . . . . . . . . . . . . . . . . . . . . 18

第三章異常偵測的基本架構
3.1 方塊切割 . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 特徵描述子. . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 圖像特徵描述子. . . . . . . . . . . . . . . 24
3.2.2 方向特徵描述子. . . . . . . . . . . . . . . 25
3.2.3 速度特徵描述子. . . . . . . . . . . . . . . 26
3.3 訓練模型 . . . . . . . . . . . . . . . . . . . . . . . 27
3.4 測試步驟 . . . . . . . . . . . . . . . . . . . . . . . 29
3.5 結語 . . . . . . . . . . . . . . . . . . . . . . . . . . 30
第四章模擬與實驗
4.1 PASCAL 方法 . . . . . . . . . . . . . . . . . . . . 31
4.2 UCSD ped1 . . . . . . . . . . . . . . . . . . . . . . 34
4.3 UCSD ped2 . . . . . . . . . . . . . . . . . . . . . . 39
第五章結論與未來展望
5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.2 未來展望. . . . . . . . . . . . . . . . . . . . . . . 42

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