臺灣博碩士論文加值系統

在電腦視覺的領域中，應用背景相減於移動物體的偵測一直是有效且被廣為採用的方法，然而偵測的效果在實際的應用上常因背景環境的變化而減少其正確性，在許多研究中都指出，若可在不受限制的環境中建立適當的背景模型，將對移動物體的偵測有很大的幫助。因此，本研究的目標就是要在正常光線下，針對常見的環境變異情況，建構並維護一個健全的背景模型。利用此背景模型對視訊中各畫面進行背景相減（background subtraction），並考慮陰影特性去除陰影，抽取理想的前景偵測結果，以利開發後續智慧型視覺監控系統之技術。
本論文所提的背景模型建構與維護方法主要分成三個架構：一、初始化背景模型：自動建立起始背景模型以及後續背景相減時所需的門檻值。二、訓練背景模型：透過不斷學習背景細微變化，維護背景模型與背景的一致性。三、置換背景模型：一旦發現較大的背景變化，則持續收集畫面影像，並根據環境的敏感性，作為置換的依據。我們使用這樣的方式建立一套線上移動物體偵測系統，並以多種狀況的環境變化視訊測試均顯示此研究所提方法之實用性。

Background subtraction is a useful and effective method for detecting moving objects in computer-vision applications. However, the variant environments make the detection result to be unsatisfactory. In order to improve the detection accuracy, an appropriate background model must be constructed and maintained to accommodate to the changed environment. In this research, a robust background model maintenance mechanism is proposed and used to implement a moving object detection module.
The proposed mechanism includes three phases: initial background model construction, sustained background model adjustment, rapid background model replacement. In the first phase, an initial background model is constructed from an unfiltered video stream. In the second phase, the background model is adjusted continually according to the gradual change of environment. If a sudden change of environment occurs, the current background model must be replaced rapidly by a new background model in the third phase. The new background model is trained from some video frames collected in the second phase.
Finally, a moving object detection system is performed by applying a background subtraction approach and a shadow elimination technique. By examining in several variant environments, the Fine experimental results illustrate the practicability of the proposed background model maintenance mechanism.

中文摘要iv
英文摘要v
誌謝vi
目錄vii
圖目錄ix
表目錄xi

第一章 緒論1
1.1 研究背景與動機1
1.2 研究目的2
1.3 研究限制3
第二章 文獻探討5
2.1 分離前景與背景7
2.2 建構背景模型9
2.3 維護背景模型12
2.4 建構及維護背景模型的問題13
第三章 建構及維護背景模型15
3.1 初始化背景模型15
3.2 背景相減19
3.3 差異圖分析20
3.4 維護背景模型22
第四章 置換背景模型25
4.1 記錄圖分析28
4.2 建構固定背景模型32
4.3 建構擺動背景模型33
4.4 因應背景模型結構改變的方法調整37
第五章 偵測移動物體38
5.1 物體的偵測39
5.2 物體的移動40
5.3 去除影子41
第六章 實驗與評估44
6.1 實驗討論44
6.2 方法評估49
第七章 結論與未來展望53
參考文獻54

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