在非重疊視角多相機下利用傳遞擴充特徵之人員追蹤串接技術

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研究生：	陳科引 Chen, Ke-Yin
論文名稱：	在非重疊視角多相機下利用傳遞擴充特徵之人員追蹤串接技術 Human Tracking using Augmented Feature Propagation for Multiple Cameras with Non-overlapping Views
指導教授：	黃仲陵 Huang, Chung-Lin 張意政 Chang, I-Cheng
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2010
畢業學年度：	99
語文別：	英文
論文頁數：	84
中文關鍵詞：	多攝影機、非重疊視野、追蹤
外文關鍵詞：	Multiple Cameras, Non-overlapping Views, Tracking
相關次數：	點閱：1 下載：0
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由於監視系統的普及與電腦視覺的迅速發展，傳統只利用人工來觀看的監視系統已經不符合人力成本與工作效率。因此新型態的多重攝影機監視系統，可以經由電腦視覺的方式，在廣大的監視區域可以自動偵測移動物件與連續追蹤個別人物，更進一步的辨識移動人物之身分與人物的行為分析，之後還可以增加異常行為分析。希望以完全數位化的方式來設計安全監視系統，可以減少監控者粗心而導致危險之異常事件。
本論文主要是藉由攝影機錄製的影片來訓練出區域(exit/entry)之間的時間與空間關係，利用時間與空間的資訊，加上各個人物之間的色彩相似度，來找出人物在不同攝影機之間的相關性。除此之外，這篇論文中主要貢獻的研究是在多攝影機系統中增加一個擴充特徵，而這個特徵(如:人臉、步伐、身高…)不是每一台攝影機都可以成功抓取，在這樣的條件下，我們無法直接使用擴充特徵去做人物的對應，因為有一些的攝影機是無法擷取到擴充特徵，所以我們的研究不同於之前的研究，將攝影機的架設方式統一都可擷取新的擴充特徵，觀察辨識率提升多少，而我們提出擴充特徵可以在不同攝影機之間做傳遞，再利用傳遞之後擴充特徵來做錯誤路徑的校正。

Due to tremendous amount of crime activities have occurred recently, Security has become the important issue. Surveillance systems have been installed in home, airports, railway stations, department stores and other places. The traditional surveillance systems have required high human cost but with low efficiency. Therefore, new types of multi-camera surveillance system can automatically detect and continuously track the moving objects based on computer vision technology.
Under some circumstances, the tracking of human objects may fail because of light change, unusual behaviors, clothes change between cameras, or staying in the blind region for a long time. It will generate path discontinuity. In this thesis, we make use of the relaxed features matching to solve the problem of missing object tracking. Furthermore, because of the viewing angle of the cameras or the objects’ moving directions are different, the captured features are not the same. We propose a concept indicating that the feature can be propagated between different scenes. The augmented feature can be used for cascading the objects paths. We propose Augmented Feature to correct the path by using the similar appearance of the objects across multiple cameras.

CONTENTS
Chapter 1 Introduction    ………………………………1
1     Motivation      ………………………………1
2     Related Works   ………………………………3
3     System Overview ………………………………5
4     Organization of the Thesis…………………9
Chapter 2 Pre-processing and Feature Extraction for
         Single Camera Capturing ……………………10
1    Foreground Extraction ………………………10
1.1    Background Subtraction………………………11
1.2    Morphological Filtering ……………………12
1.3    Labeling & Size Filter………………………13
1.4    Shadow Removal…………………………………14
2    Face Detection…………………………………16
3    Entry/Exit Zone Identification……………17
3.1    Gaussian Mixture Model………………………17
3.2    Expectation Maximization Algorithm………18
Chapter 3 Object-based People Tracking across
          Multiple Cameras ……………………………22
1    Observation Model ……………………………22
1.1    Color Histogram ………………………………22
1.2    Color Calibration ……………………………24
2    Learning Camera Network Topology…………26
3    Initial Path Construction …………………28
Chapter 4 Error Path Modification……………………32
1    Relaxed Feature Matching……………………32
2    Path Correction used Augmented Features 38
2.1    Error Path Detection Function ……………38
2.2    Feature Propagation Criterion ……………43
2.3    Path Segment Correspondence Analysis……47
2.4    Path Correction Using Augmented feature of
         Face………………………………………………52
Chapter 5 Experimental Results ………………………59
1    MFC Interface …………………………………60
2    Single Person Tracking………………………61
3    Multiple Persons Tracking …………………64
Chapter 6 Conclusions……………………………………81
References …………………………………………………82

                                

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