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研究生:江敬群
研究生(外文):Ching-Chun Chiang
論文名稱:利用動態估測預測物體位置並應用於以色彩為基礎的前景物體偵測
論文名稱(外文):Object Location Prediction Based on Motion Estimation with Application on Color-Based Foreground Object Detection
指導教授:連豊力
指導教授(外文):Feng-Li Lian
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
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:2007
畢業學年度:95
語文別:英文
論文頁數:92
中文關鍵詞:前景偵測物體位置預測影像處理影像分析
外文關鍵詞:foreground detectionobject location predictionimage processingimage analysis
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在很多電腦視覺以及機器視覺的應用當中,一些前景偵測的方法經常被使用來做為前置處理。影像之中有許多的特性被用來作為分辨前景以及背景的依據,其中物體的動態資訊可以有效的分辨出移動中的物體。
雖然動態資訊非常的有效,但是取得整張影像的動態資訊需要非常大的計算量。在過去,只有一些快速的演算法用應用在動態估測的處理上面來增加估測的速度。事實上,並非整張影像裡面的所有動態都非常重要,只有前景物體的動態才真正是被前景偵測系統所需要,而背景的動態則並非必要的,這意味著動態估測的處理不需要實行在背景的區域。
這個研究提出了一個在影片中預測前景物體位置的方法。這個方法同時使用了動態以及交通密度來預測前景物體在影像中的位置。其中物體的動態可由動態估測獲得而交通密度則可以由過去的判斷結果得到。
一個前景偵測的程式被設計來驗證這個預測方法。對於移動以及大小改變的物體的預測能力將藉由一些特殊的影片來解釋。最後,使用使用預測方法的優勢將藉由三個不同的輸入影片加以說明。
Many computer vision and machine vision applications employ some foreground detection methods as the first stage for detecting object location. Many characteristics of image data have been used to segment images into background and foreground elements. Motion is effective information for detecting moving objects in two continuous images.
Although motion is helpful to detect foreground objects, it requires a heavy computational load when detecting all motions of an image. In previous applications, some fast search algorithms are proposed to reduce the computational load of motion estimation. In fact, not all motions are important in an image. Only the foreground object motion is required in a foreground detection system. The background motion is not necessary for detecting foreground, and it means that the motion estimation process has no need to be applied in the background area.
In this research, a method is proposed for predicting foreground object location in a video. The method uses both motion and traffic density to predict object location in an input image. Motion is obtained by motion estimation, and traffic density is obtained by the analysis of historical detection results.
A program of foreground detection is designed to verify the prediction method. The prediction capabilities with moving and size-changing object are explained by the experiments with some special videos. Finally, the advantages of using the prediction method are illustrated through the experiment with three different input videos.
摘要 I
ABSTRACT III
CONTENTS V
LIST OF FIGURES IX
LIST OF TABLE XVII
CHAPTER 1 1
INTRODUCTION 1
1.1 Motivation 1
1.2 Fundamental Components of Foreground Detection 3
CHAPTER 2 9
LITERATURE SURVEY 9
2.1 Models and Feature for Building Background 10
2.2 Motion Estimation 11
2.3 Per Pixel Model and Region-based Model 12
2.4 Applications of Foreground Detection 12
CHAPTER 3 15
FUNDAMENTAL KNOWLEDGE OF IMAGE ANALYSIS AND PATTERN RECOGNITION 15
3.1 Image Acquisition 16
3.1.1 Digital Image Format 16
3.1.2 Spatial Filtering 17
3.1.3 HSI Color Space 20
3.2 Maximum Likelihood Estimation 21
3.3 Morphological Image Process 24
3.3.1 Dilation 25
3.3.2 Erosion 27
3.3.3 Opening and Closing 29
CHAPTER 4 33
OBJECT LOCATION PREDICTION IN VIDEO FLOW 33
4.1 Predict Method Based on Object Motion 34
4.1.1 Motion Estimation 35
4.1.2 Adaptive Prediction Block Size 43
4.2 Predict Method Based on Traffic Density 45
4.3 Combination of Motion Estimation and Traffic Density Map 47
CHAPTER 5 51
EXPERIMENTAL RESULTS OF FOREGROUND DETECTION SYSTEM 51
5.1 Experimental Environment and Framework of the System 51
5.2 The Classifier Used in the Experiment 59
5.3 Experimental Results of Object Location Prediction 62
5.4 Morphological Image Processing Results in the Experiments 72
5.5 The Experimental Results of Foreground Segmentation 73
CHAPTER 6 87
CONCLUSION AND FUTURE WORK 87
6.1 Conclusion 87
6.2 Future Work 88
REFERENCES 89
[1: Gonzalez and Woods 2002]
R.C. Gonzalez and R.E. Woods, “Digital Image Processing,”2nd Edition, Prentice Hall, 2002
[2: Duda et al. 2001]
R. O. Duda, P.E. Hart and D. G. Stork, “Pattern Classification,” Wiley-Interscience, 2001
[3: Bhaskaran and K. Konstantinides 1997]
V. Bhaskaran and K. Konstantinides, “Image and Video Compression Standards,” Kluwer Academic Publishers, 1997
[4: Shi and Sun 2000]
Y.Q. Shi and H. Sun, “Image and Video Compression for Multimedia Engineering,” CRC Press, 2000
[5: Lee et al. 2003]
D.S. Lee, J.J. Hull and B. Erol, “A Bayesian Framework for Gaussian Mixture Background Modeling,” IEEE International Conference on Image Processing, Vol. 2, Barcelona, Spain, pp. 973-976, Sep. 2003
[6: Elgammal et al. 2000]
A.M. Elgammal, D. Harwood and L.S. Davis, “Non-parametric Model for Background Subtraction,” European Conference on Computer Vision, Vol. 2, Dublin, Ireland, pp. 51-767, Jul. 2000
[7: Kumar et al. 2002]
P. Kumar, K. Sengupta and A. Lee, “A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system,” IEEE International Conference on Intelligent Transportation Systems, Singapore, pp. 100-105, Sep.2002
[8: Huang and Wu 1998]
W.C. Huang and C. H. Wu, “Adaptive Color Image Processing and Recognition for Varying Backgrounds and Illumination Conditions,” IEEE Transactions on Industrial Electronics, Vol. 45, pp. 351-357, Apr. 1998
[9: Smolic et al. 1999]
A. Smolic, T. Sikora and J.-R. Ohm, “Long-Term Global Motion Estimation and Its Application for Sprite Coding, Content Description, and Segmentation,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, pp. 1227-1242, Dec. 1999
[10: Giaccone et al. 2000]
P.R. Giaccone, D. Tsaptsinos and G.A. Jones, “Foreground-background segmentation by cellular neural networks,” IEEE International Conference on Pattern Recognition, Vol. 2, Barcelona, Spain, pp. 438-441, Sep. 2000
[11: Xu et al. 2003]
N. Xu, R. Bansal and N. Ahuja, “Object Segmentation Using Graph Cuts Based Active Contours,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, Madison, Wisconsin, pp. 46-53, Jun. 2003
[12: Lin et al. 2005]
X. Lin, B. Cowan and A. Young, “Model-based Graph Cut Method for Segmentation of the Left Ventricle,” IEEE-EMBS International Conference on Engineering in Medicine and Biology Society, Shanghai, China, pp. 3059-3062, Sep. 2005
[13: Lombaert et al. 2005]
H. Lombaert, Y. Sun, L. Grady and C.Y. Xu, “A Multilevel Banded Graph Cuts Method for Fast Image Segmentation,” IEEE International Conference on Computer Vision, Vol. 1, Beijing, China, pp. 259-265, Oct. 2005
[14: Shafarenko et al. 1997]
L. Shafarenko, M. Petrou and J. Kittler, “Automatic Watershed Segmentation of Randomly Textured Color Images,” IEEE Transactions on Image Processing, Vol. 6, pp. 1530-1544, Nov. 1995
[15: Kumar et al. 2005]
P. Kumar, S. Ranganath, W. Huang and K. Sengupta, “Framework for Real-Time Behavior Interpretation From Traffic Video,” IEEE Transactions on Intelligent Transportation Systems, Vol. 6, pp. 43-53, Mar. 2005
[16: Tai and Song 2004]
J.C. Tai and K.T. Song, “Background Segmentation and its Application-to Traffic Monitoring Using Modified Histogram,” IEEE International Conference on Networking, Sensing and Control, Vol. 1, Taipei, Taiwan, pp. 13-18, Mar. 2004
[17: Lin et al. 2005]
X. Lin, B. Cowan and A. Young, “Model-based Graph Cut Method for Segmentation of the Left Ventricle,” IEEE-EMBS International Conference on Engineering in Medicine and Biology Society, Shanghai, China, pp. 3059-3062, Sep. 2005
[18: Song et al. 2006]
Z. Song, N. Tustison, B. Avants and J. Gee, “Adaptive Graph cuts with Tissue Priors for Brain MRI segmentation,” IEEE International Conference on Biomedical Imaging: Macro to Nano, Arlington, Virginia, pp. 762-765, Apr. 2006
[19: Hussein et al. 2006]
M. Hussein, W. Abd-Almageed, R. Yang and L. Davis, “Real-Time Human Detection, Tracking, and Verification in Uncontrolled Camera Motion Environments,” IEEE International Conference on Computer Vision Systems, New York, New York, pp. 41-41, Jan. 2006
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1. 吳仁和、董和昇、王麗貞(1999):〈以實驗法探討資訊凸顯方式對使用者嬝祀Z效之影響〉,《資訊管理學報》,5(2),頁111-122。
2. 莊政(1994):〈試論社會科學的研究方法〉,《人文及社會學科教學通訊》,4(4),頁128-139。
3. 陳百齡(2001):〈從國科會傳播專題計畫提案看學門發展生態:1966-2000年〉,《新聞學研究》,67,頁25-49。
4. 陳彰儀、林新沛(1984):〈問卷長度、切要性、研究者權威性及追蹤聯繫對郵寄問卷、回收率的影響〉,《中華心理學刊》,26(2),頁77-84。
5. 黃俊英(1981):〈郵寄問卷調查的回件率問題〉,《國立政治大學學報》,(43),頁141-153。
6. 黃懿慧(2001),〈90年代公共關係研究之探討-版圖發展變化與趨勢〉,《新聞學研究》,67,頁51-86。
7. 張維安(2001):〈文字模式線上訪談的特質及其限制〉,《資訊社會研究》,(1),頁279-297。
8. 張紘炬(1992):〈郵寄問卷回收率問題之探討〉,《研考雙月刊》,16(1),頁13-21。
9. 鄒浮安(2001):〈敘述研究方法之探討〉,《雄中學報》,(4)。
10. 蔡嘉哲(1994):〈運用電腦科技實施問卷調查〉,《教學科技與媒體》,(15),頁48-51。
11. 劉駿州(1994):〈實證、批判、詮釋-三大方法典範之初探〉,《新聞學研究》,(48),頁153-167。
12. 蘇蘅、吳淑俊(1997):〈電腦網路問卷調查可行性及回復者特質的研究〉,《新聞學研究》,(54),頁75-100。