臺灣博碩士論文加值系統

近年來隨著對於環境安全的需求越來越受到重視，視訊監控技術的研究也越來越重要。
一般傳統的視訊監控技術通常都存在著一些缺點。

第一，
在一個光線變化劇烈以及擁擠複雜的環境下，目標物容易偵測錯誤，
特別是當光線反射以及背光現象發生時，更容易影響目標物的偵測。
第二，
動態紋理特徵常常會製造多餘的移動雜訊，如：樹葉的晃動、水面的波紋，
進而影響到被偵測物體的真實性而造成過多的錯誤偵測。
第三，
當移動目標由Ａ相機消失後，進而在某段時間後由Ｂ相機出現，
再這期間需要一個交替移交監控權的機制。
第四，
錯誤的目標物偵測將會降低預測目標物的準確性。

所以在此研究中，我們將利用pixel-wise temporal probability background model,
dynamic texture model, color-based difference projection, handoff scheme
來解決上述之問題。

從實驗的結果發現，本篇研究能在一個擁擠環境下正確的追蹤目標物，並且每秒約可處裡10張frame。

With the great demand of constructing a safe and security environment, video surveillance is becoming more and more important.
Conventional video surveillance systems often have several shortcomings.
First, object detection can’t have high accuracy under the illumination variation environment or clustering background. Especially, the surface reflection and back-lighted problems can influence the object detection seriously.
Second, some dynamic textures e.g., moving leaves, water ripples, will influence the reliability of object detection.
Third, when an object leaves from the scene of camera A and then appears on the scene of camera B, a handoff scheme is seldom considered.
Finally, the tracking efficiency and precision are reduced by the inaccurate foreground detection.
In this study, the pixel-wise temporal probability background model, the dynamic texture modeling, color-based difference projection, and handoff between two cameras are proposed to improve the above mentioned problems.
Experimental results show that the objects on the crowd scene may be detected correctly and with detecting rate above 10 fps.

中文目次：
目錄
摘要.............................................................1
致謝.............................................................2
目錄.............................................................3
第一章 簡介.......................................................4
第二章 以像素點為基礎之時間背景機率模型..............................7
第三章 動態紋理模型................................................8
第四章 多模式目標物追蹤............................................9
第五章 換手機制..................................................10
第六章 實驗結果..................................................11
第七章 結論......................................................12

英文目次：

Abstract..............................................................1
Contents..............................................................2
Chapter 1 Introduction................................................3
Chapter 2 Temporal Probability Background Model.......................7
2.1 Pixel-Wise Temporal Probability Model.......................7
2.2 Foreground Detection Rule...................................9
Chapter3 Dynamic Texture Modeling....................................13
3.1 Modified Local Binary Pattern..............................13
3.2 Foreground Detection using Modified LBP....................14
3.3 Foreground Variation.......................................16
Chapter 4 Multi-Mode Target Tracking.................................18
4.1 Multi-Mode Target Tracking Scheme..........................18
4.2 Target Segmentation........................................20
4.3 Principal Axis and Ground-point Detection..................21
4.4 Target Tracking using Kalman Filter........................22
Chapter 5 Handoff Scheme.............................................23
5.1 Trajectory Analysis Model..................................23
5.2 Construction of Handoff Scheme.............................24
Chapter6 Experimental Results........................................27
6.1 Object Extraction..........................................27
6.2 Moving Object Filtering using Dynamic Texture Model........29
6.3 Multi-mode Target Tacking Scheme...........................30
6.4 Principle-Axis and Ground Point Tracking...................33
6.5 Multiple Cameras Handoff method............................34
6.6 Close-up Tracking using Cooperative Cameras................38
Chapter 7 Conclusion.................................................40
Reference............................................................41

Reference.
[1] J. Assfalg, A. D. Bimbo, and M. Hirakawa, “A mosaic-based query language
for video databases,” IEEE International Symposium on Visual Language,
2000, pp. 31-38.
[2] S. Dagtas, W. Al-Khatib, A. Ghafoor and R. L. Kashyap, “Models for
motion-based video indexing and retrieval,” IEEE Transactions on Image
Processing, Vol. 9, No. 1, Jan. 2000, pp. 88-101.
[3] M. Petkovic and W. Jonker, “Content-based video retrieval by integrating
spatio-temporal and stochastic recognition of events,” IEEE Workshop on
Detection and Recognition of Events in Video, July 2001, pp. 75-82.
[4] I. Haritaoglu, D. Harwood, and L. S. Davis, “W4：Who? When? Where? What?
A real-time system for detecting and tracking people,” IEEE International
Conference on Automatic Face and Gesture Recognition, April 1998, pp. 222-
227.
[5] C. W. Lin, Z. H. Ling and Y. C. Chang, “Compressed-domain fall incident
detection for intelligent home surveillance”, IEEE International
Symposium on Circuits and Systems 2005, ISCAS, Vol. 4, May 2005, pp.
23-26.
[6] J. S. Hu, T. M. Su and S. C. Jeng, “Robust background subtraction with
shadow and highlight removal for indoor surveillance,” IEEE International
Conference on Intelligent Robots and Systems, Oct. 2006, pp. 4545-4550.
[7] J. R. Renno, T. Orwell and G. A. Jones, “Evaluation of shadow
classification techniques for object detection and tracking”, IEEE
International Conference on Image Processing (ICIP) 2004, Vol. 1, pp.
24-27.
[8] E. Polat, M. Yeasin and R. Sharma, “Multiple complex object tracking
using a combined technique”, IEEE International Conference on Pattern
Recognition, Vol. 2, Aug. 2002, pp. 11-15.
[9] A. Elgammal, D. Harwood and L. Davis, “Non-parametric model for
background subtraction,” in Proceedings of the 6th European Conference
on Computer Vision, 2000, pp. 751-767.
[10] X. Dai and S. Khorram, “Performance of optical flow techniques,”
IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, 1992, June 1992, pp. 236-242.
[11] R. Jain, W. Martin and J. Aggarwal, “Segmentation through the detection
of changes due to motion,” Compute Graph Image Process 11, 1979, pp. 13–
34.
[12] Y. Ren, C. S. Chua and Y. K. Ho, “Motion detection with nonstationary
background,” Machine Vision and Application, Vol. 13, No. 5-6, Mar. 2003,
pp. 332–343.
[13] C. C. Lien and S. C. Hsu, “The target tracking using the spatial-temporal
probability model,” IEEE International Conference on Nonlinear Signal and
Image Processing, NSIP 2005, May 2005, pp. 34-39.
[14] C. Wren, A. Azarbayejani, T. Darrell and A. Pentland, “Pfinder: real-time
tracking of the human body,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, Vol. 19, No. 7, July 1997, pp. 780-785.
[15] M. Xu, J. Orwell, L. Lowey and D. Thirde, “Architecture and algorithms
for tracking football players with multiple cameras” Image and Signal
Processing, IEE Proceedings, Vol. 152, Issue 2, April 2005, pp. 232-241.
[16] K. Nummiaro, E. K. Meier and L. J. V. Gool, “An adaptive color-based
particle filter” Image Vision Computing, Vol. 21, Issue. 1, 2002, pp.
99-110.
[17] W. Hu, M. Hu, X. Zhou, Tieniu Tan, “Principal axis-based correspondence
between multiple cameras for people tracking” IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, April 2006,
pp. 663-671.
[18] D. Salmond, “Target tracking: introduction and Kalman tracking filters,”
IEEE Target Tracking: Algorithms and Applications, Vol. 2, 2001, pp. 1/1-
1/16.
[19] E. Alpaydin, Introduction to Machine Learning. MIT Press, Cambridge 2004.
[20] Y. Yang and M. Levine, “The background primal sketch: an approach for
tracking moving objects,” Machine Vision and Applications, 1992, Vol. 5,
pp. 17-34.
[21] C. C. Lien, C. C. Han, J. H. Tang, J. C. Wang, “A multiple target
tracking system with close-up zooming using the cooperative cameras,”
Journal of Information Technology and Applications, Vol. 1, No. 3, Dec.
2006, pp. 215-227.
[22] T. Ojala, M. Pietika¨inen, and T. Ma¨enpa¨a¨, “Multiresolution Gray Scale
and Rotation Invariant Texture Analysis with Local Binary Patterns,”
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7,
pp. 971-987, July 2002.