結合人物追蹤與步態辨識的智慧型視訊監控系統

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姓名

羅偉瑜(Wei-Yu Lo) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

結合人物追蹤與步態辨識的智慧型視訊監控系統
(A Smart Surveillance System with user-tracking and Human Gait Recognition)

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摘要(中)

目前智慧型監控系統多只具備追蹤或是辨識單一功能，且追蹤功能容易受限於收發訊號距離和障礙物，本論文所提出的一個新的智慧型監控系統同時具備追蹤定位和身分辨識的功能。追蹤定位系統架構由兩個部分組成，首先是追蹤定位的仰賴於視訊定位方法，利用投影方法得知影像座標和真實環境座標之間的關係，並且利用此關係求出目前人物的座標位置。辨識方面利用步態辨識方法去辨識出行人的身分。無論是追蹤定位或是步態辨識功能都不需使用者配戴隨身裝置，環境部分也無須架設大量的監控錄影機，大幅節省架設成本。從資料庫驗證和實錄影片進行測試時得到，定位準確度可達到95%以上，辨識正確率也可達到87%。兩樣功能結合起來的貢獻度，不只具備單一功能的特點，還多了雙重確認、節省成本、降低環境限制的其他優點。

摘要(英)

Most current smart monitoring systems feature only one function (i.e., tracking or identification function). In addition, the identification function is prone to the interference of obstacles and the distance between the signal sender and recipient. In this study, a novel smart monitoring system featuring both tracking–positioning and identification functions was developed. The proposed system involved two components. First, a video positioning method was adopted for tracking the device location. This method entailed employing a projection method to convert image coordinates to real-world coordinates, and accordingly, to determine the coordinates of the person in question. The identity recognition function of the proposed system used the gait cycle of a person to reveal his or her identity. Therefore, neither the tracking–positioning nor gait recognition function required the person in question to wear portable devices or entailed installing a large number surveillance cameras in the environment; this substantially lowered installation costs. Tests performed using a database and recorded videos verified that the smart monitoring system introduced in this study demonstrated a position accuracy and identification accuracy of 95% and 87%, respectively. Overall, the proposed system offers two monitoring functions, enables double confirmation, reduces costs, and overcomes environmental restrictions.

關鍵字(中)

★ 步態辨識
★ 定位

關鍵字(英)

★ gait
★ homography matrix

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 ix
第一章、緒論 1
1.1研究背景 1
1.2文獻回顧 3
1.3研究動機 4
1.4研究目的和方法 4
第二章、相關技術回顧 9
2.1前景影像切割 9
2.1.1建立背景模型 9
2.2空間轉換 14
2.3步態特徵抽取 16
2.4步態辨識 19
第三章、系統設計 22
3.1系統架構 22
3.1.1前景影像切割 23
3.1.2空間座標轉換 24
3.1.3步態特徵抽取 24
3.1.4步態辨識 25
3.2離散事件系統建模 25
3.2.1步態追蹤辨識系統建模 29
3.2.2前景影像切割系統建模 30
3.2.3空間轉換建模 31
3.2.4步態特徵抽取建模 31
3.2.5步態辨識建模 33
3.2.6主要的狀態與動作 33
第四章、實驗方法與結果 35
4.1實驗 36
4.1.1步態資料庫 36
4.1.2辨識正確率的評估 38
4.2影像定位實驗 39
4.2.1影像定位實驗環境 39
4.2.2影像定位的正確率 42

4.3步態辨識實驗 44
4.4實驗結果與討論 47
第五章、結論與未來方向 50
5.1結論 50
5.2未來方向 51
參考文獻 52

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2018-4-16

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