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論文名稱(外文):Abnormal Moving Object Detection under Various Enviroments Using Self-Organizing Incremental Neural Networks
指導教授(外文):Chin-Shyurng FahnHsien-Chou Liao
外文關鍵詞:anomaly detectionself-organizing incremental neural networkvideo surveillance
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本論文提出一個可應用於在各種環境中,即時自動偵測出異常運動物體的方法。首先偵測場景上的運動物體,我們利用高斯混合模型偵測前景,並透過陰影濾除消除前景陰影,再利用Blobs檢測出運動物體。我們提出一個結合卡爾曼濾波器的改良式Mean Shift演算法對這些運動物體進行追蹤,最後使用卡爾曼濾波器平滑軌跡資料。
Abnormal moving objects detection is an essential issue for video surveillance. In order to judge whether the behavior of objects is abnormal, such as pedestrians walk back and forth, walk across the street, or scooters drive the wrong way, the main method is through computer vision technique to analyze objects as pedestrians, cars, and so on in video. Traditional abnormal moving objects detection aims at particular circumstances or requirement to predefine particular detection rules which the application of abnormal moving objects detection is restricted. Besides, if numerous abnormal moving objects are detected at the same time, surveillance system is overloaded with operation. Owing to this reason, in this paper, we expect to design a set of learning model which does not predefine abnormal rules and can detect a variety of abnormal moving objects automatically in different environments.
To achieve the above goal, the first thing is to detect the moving objects in video. The proposed method in this paper utilizes Gaussian Mixture Model (GMM) to detect foreground objects and remove shadows of objects by shadow removal. Then, adoptive mean shift algorithm with Kalman filter is proposed to track these moving objects. Finally, Kalman filter is used to smooth trajectory.
After collecting the trajectories of moving objects, abnormal moving object detection process proceeds. At first, for this trajectory information, take advantage of Self-Organizing Incremental Neural Network (SOINN) to learn and build a normal trajectory model which is a foundation to determine whether follow-up moving objects are abnormal. The average learning time is 7 to 55 seconds.
The experiment monitors and analyzes different circumstances, such as School campus, roads, and one-way street. The system based on the proposed method can detect abnormal moving objects with the accuracy 100% in school campus, 98.3% in roads, and 98.8% in one-way street. The overall execution time is short and about 0.033 to 0.067 seconds, and it can be executed in real-time.
中文摘要 i
Abstract ii
致謝 iv
Table of Contents v
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Motivation 1
1.3 System Description 2
1.4 Thesis organization 3
Chapter 2 Related Works 4
Chapter 3 Moving Object Detection and Tracking 10
3.1 Foreground Detection 10
3.1.1 Background model 10
3.1.2 Shadow Removal 13
3.2 Moving Object Tracking 15
3.2.1 Moving Object Detection and Blobs Tracking 15
3.3 Multiple Objects Tracking with Occlusion Handling 19
3.3.1 Kalman Filter 20
3.3.2 Mean Shift Algorithm 21
3.3.3 Proposed Modified Mean Shift Method 23
3.4 Handling of a Missed Tracking Object 26
3.5 Trajectory Post Processing 28
Chapter 4 Abnormal moving object detection 30
4.1 Trajectory Feature Extraction 30
4.2 Self-Learning Using SOINN 32
4.3 Abnormal moving object detection 39
Chapter 5 Experimental Results and Discussions 42
5.1 Experimental Setup 43
5.2 Results of Moving Object Tracking 44
5.3 The Results of SOINN Learning Trajectory 51
5.4 The Results of Abnormal Moving Object detection 53
Chapter 6 Conclusions and Future Works 61
6.1 Conclusions 61
6.2 Future Works 62
References 63
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