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研究生:鄭伯南
研究生(外文):Po-Nan Cheng
論文名稱:模糊推論在保全系統中自動偵測與辨識入侵者之應用
論文名稱(外文):The Application of Fuzzy Inference to the Automatic Detection and Identification of Intruders in Security System
指導教授:黃有評黃有評引用關係
指導教授(外文):Yo-Ping Huang
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:中文
論文頁數:55
中文關鍵詞:模糊推論影像差值法影像處理
外文關鍵詞:Fuzzy inferenceImage subtractionImage processing
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受惠於科技和技術的進步,使得一些保全系統之類的監控設備價格逐漸被一般大眾所接受,開始慢慢願意在家中裝設保全系統,保護家人和財產的安全並防止小偷的入侵。
本論文運用攝影機結合影像處理和模糊推論的技術,建構一個主動監控系統,以判斷畫面中是否有入侵者,並且透過無線網路將訊息傳送到監控者的手持裝置,讓監控者在任何時間、任何地點均可知道是否有非法的入侵者正在系統監視範圍內。我們使用模糊推論的原因在於一般的警報系統,稍有風吹草動造成感應器或是畫面的變動,就會觸動保全系統,頻繁的錯誤警報,容易造成監控者習慣於『狼來了』的迷思,久而久之便放鬆了對於保全系統警報所應有的警覺性,所以我們希望能夠透過影像特徵的擷取和模糊推論的結合,將所得到的影像加以分析,提高對使用者發出入侵者警報的正確率。
在本論文中,將所擷取的特徵用模糊推論來推論入侵者是否是人類或僅是一般的貓狗,選擇人和貓狗是因為在一般的家庭最有可能出沒的就是人和貓狗,所以我們在系統中設定推論入侵者和動物的規則,透過所擷取的影像特徵,然後和模糊規則相比較,推論入侵者的種類,未來若要擴大系統的辨識範圍,則只需要加入新的規則即可,而不需重新建構一個新系統,因此所設計之系統具有很方便的擴充性。
所提系統利用影像處裡與模糊理論技術,設計出一套自動偵測與辨識系統,我們除了提出判斷入侵者是人或是動物之模糊推論法則外,亦實際驗正如何將入侵訊息以簡訊的方式傳送至使用者之手機或手持裝置上。
A home security system is designed to detect the illegal intruders and warns the home owner. A home security system is usually equipped with camera, sensor, video recorder, loudspeaker, and other electronic equipments. When home owners were sleeping or left the house, they can start the security system to monitor their house and trigger the alarm if there were illegal intruders. Most security systems will send alarm messages to users when the sensors were triggered, but cannot identify what the intruder is. If the security systems often make the false alarm caused by the animals, people may relax their vigilance as time passes.
In order to solve those common problems of traditional home security systems, we combine the image recognition, motion detection, image processing, and fuzzy inference to identify whether the illegal intruder is human, cat or dog. Human, dog, and cat are the most likely appearance in the house. We use a camera to capture the images and analyze those incoming images. After obtaining the foreground object from those incoming images, we can derive its characters and then apply the fuzzy inference model to recognizing the identified object. We design the fuzzy rules to identify whether the object is human or animal. Another advantage in our model is that if we want to add more functions in, we just add new rules in the rule base.
Table of Contents
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 RELATED WORK 3
2.1 Overview 3
2.2 Relative Research 3
2.3 Fuzzy Set Theory 4
2.3.1 Fuzzy set 4
2.3.2 Fundamental operations of fuzzy set 6
2.3.3 Fuzzy reasoning 9
2.3.4 Fuzzy inference system 10
CHAPTER 3 SYSTEM EVALUATION 15
3.1 Overview 15
3.2 Initial Background 15
3.3 Image Subtraction 17
3.4 Automatic Threshold 19
3.5 Dilation and Erosion 23
3.6 Removing the Shadows 25
3.7 Connected Components Labeling 26
3.9 Edge Detection 31
3.10 Image Skeleton 32
3.11 Identification 34
CHAPTER 4 SYSTEM IMPLEMENTATION AND EXPERIMENTAL RESULTS 37
4.1 System Architecture 37
4.2 System Implementation 38
4.3 Experimental Results 46
CHAPTER 5 CONCLUSION AND FUTURE WORK 51
REFERENCES 53
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