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研究生:林志展
研究生(外文):Chih-Chang Lin
論文名稱:應用尺度不變特徵轉換演算法實作之物件保全系統
論文名稱(外文):Implementation of an Object Security System based on Scale Invariant Feature Transform Algorithm
指導教授:駱至中駱至中引用關係
指導教授(外文):Chih-Chung Lo
學位類別:碩士
校院名稱:佛光大學
系所名稱:資訊學系
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:2009
畢業學年度:97
語文別:中文
論文頁數:80
中文關鍵詞:物件辨識視訊監控視訊分析尺度不變特徵轉換演算法
外文關鍵詞:object recognitionvideo surveillancevideo analyticsscale-invariant feature transform (SIFT) algorithm
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隨著社會的進步,人們越來越重視生活品質與環境治安等議題,使得智慧型視訊監控系統的開發成為一項刻不容緩的工作。本研究嘗試以電腦視覺及影像處理等相關技術,進行物品遺失事件的視訊分析,實作一套智慧型物件保全系統。電腦視覺領域中的物件辨識問題,以影像處理技術來取得物件特徵進行自動化辨識工作,其中尺度不變特徵轉換(Scale Invariant Feature Transform,簡稱SIFT)演算法所轉換的特徵值,對於影像尺度變化等影響,具有良好的辨識效果,是目前常被用來解決物件辨識問題的方法之一,因此本研究嘗試以SIFT演算法為影像特徵轉換工具,實作一套能自動偵測出物件遺失事件發生的物件保全系統。
為了達成系統即時反應的時間需求,本研究捨棄傳統SIFT演算法建立特徵資料庫的作法,提出以更新特徵值的方式來適應物件的狀態變化,並稱此改良式SIFT演算法為「自適應尺度不變特徵轉換」(Self-Adaptive Scale Invariant Feature Transform,簡稱SA-SIFT)演算法。本研究所提出的SA-SIFT演算法能適時更新物件影像的特徵值,隨著物件狀態的變化,自我調適描述目標物件的特徵向量,使系統能以少量且較接近物件當前狀態的特徵值辨識出目標物件,降低物件辨識所需耗費的時間,並提昇辨識的成功率。
實例驗證結果顯示,本研究所提出的SA-SIFT演算法,確實能隨著目標物件的狀態改變,自我調整描述目標物件的影像特徵值,平均每張監控畫面的辨識時間大約僅需300毫秒,且成功辨識率可高達90%以上,充分顯示本研究所提出的SA-SIFT演算法,確實能有效提昇物件辨識的效能及效率。
There has been a significant increase in the use of surveillance cameras in the past few years. Idyllically, the use of surveillance cameras and video monitoring systems can not only help altering their users before threatening situations getting worse, but also providing them with vital recorded evidences for security/safety events. However, one common shortcoming of traditional video surveillance systems is that they still need human operators to monitor surveillance cameras and to trace after-happening security/safety events from huge amount of video records. As more and more surveillance cameras are being mounted around our society to help stopping crime and protecting our properties, there are enormous needs of developing software solutions and other technologies to make those video surveillance systems smarter in order to streamline and automate their on-line monitoring and evidence retrieval processes.
Intelligent video analysis mechanism (also known as video analytics) is a well known solution to make video surveillance systems smarter. Object recognition technologies in video analytics are usually refer to image processing algorithms that detect and track objects of interest to look for possible security/safety threats or breaches. Recently, Scale Invariant Feature Transform algorithm (SIFT) is recognized as a very useful method for video analytics applications due to its effectiveness in dealing with scale, illumination or position changes of the object of interest. In this research, a SIFT-based intelligent video surveillance system is proposed to help monitoring objects (valuable properties) display in open spaces. Once the proposed system detects abnormal or suspicious activities via video analytics, it will provide pre-caution warning or record only video of suspicious activity. In this intelligent system, Self Adaptive SIFT (SA-SIFT) algorithm, an improved version of the original SIFT algorithm is also proposed by adding mechanism for incessant updating the template of SIFT features and adjusting the region of interest. Such enhancements are designed to extend capability of the intelligent system in object recognition with motion and scene changes.
The efficiency and effectiveness of the proposed intelligent object security system are demonstrated experimentally. After benchmarking with the original SIFT algorithm in the same experiments, it is confirmed that the proposed SA-SIFT algorithm is a more suitable method to help surveillance operators monitoring expensive or important objects via intelligent video surveillance.
中文摘要 v
Abstract vi
誌謝 vii
目錄 viii
圖目錄 ix
表目錄 xi
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究流程與論文架構 3
第二章 文獻探討 5
2.1 物件辨識問題 5
2.1.1 物件辨識的種類與求解方法 6
2.2 尺度不變特徵轉換演算法 8
2.2.1 SIFT演算法基本原理 8
2.2.2 SIFT演算法發展現況及應用限制 14
第三章 研究方法 17
3.1 智慧型物件保全系統架構 17
3.2 自適應尺度不變特徵轉換(SA-SIFT)演算法 19
3.3 感興趣區域標記 22
3.4 遮蔽判斷機制 26
3.5 SIFT特徵值回復機制 28
第四章 實證分析 30
4.1 實驗環境及設定 30
4.2 SIFT演算法之物件辨識效能分析 31
4.3 SA-SIFT演算法之物件辨識效能分析 40
4.4 SIFT演算法與SA-SIFT演算法之辨識效能比較 44
4.5 實驗成果評估與比較 71
第五章 結論 76
5.1 結論與研究貢獻 76
5.2 後續研究建議 77
參考文獻 78
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