臺灣博碩士論文加值系統

近年來，視訊的硬體擷取設備已經有大大的改善了。因為由這些硬體所擷取到的畫面有很高的品質，因此監控系統的發展已經漸漸受到研究學者們的重視了。同時，視訊監控系統也已經廣泛地應用在商業用途上。在本篇文中，我們提出兩個監控系統。其一是電梯偵測系統，該系統可以偵測人員出入電梯的狀況。其二是人員追蹤系統，該系統可以偵測街上行人的移動軌跡。上述兩個監控系統主要是由兩個步驟來達成，分別是 “分割“ 和 “追蹤”兩個步驟。在分割步驟中，我們主要是修改由Horprasert等人提出的分割演算法來將影像中的人員分割出來。我們利用色彩和明亮度的資訊來決定某個像素是屬於前景或者是背景。在追蹤步驟中，我們是根據上一個步驟的結果來追蹤移動的人員。我們也利用空間資訊來解決在追蹤過程中兩個人交會所產生的問題。

In the past years, video acquisition hardware has been considerably improved. The streams obtained from video acquisition hardware have good quality, so the area of developing video-surveillance systems has attracted researcher’s attention. At the same time, the video-surveillance systems have generally been commercially applied. In this paper, we will propose two surveillance systems. One is an elevator detection system, which can detect subjects’ coming in and going out. The other is a human tracking system, which can track pedestrians on the street. The surveillance systems are implemented utilizing two steps: segmentation and tracking. In the segmentation step, a segmentation algorithm proposed by Horprasert et al. [9] is modified to segment persons in mages. Color and brightness information are used to determine a pixel belonging to background or foreground. In the tracking step, based on the segmented result, we can track the moving persons. The spatial information is adopted to solve the intersection condition when two persons meet during the tracking time.

摘要.........................................Ⅰ
ABSTRACT.....................................Ⅱ
ACKNOWLEDGEMENTS CONTENTS....................Ⅳ
CONTENTS.....................................Ⅴ
LIST OF FIGURES..............................Ⅶ
CHAPTER 1 INTRODUCTION........................1
1.1. Motivation...............................1
1.2. Related research.........................2
1.3. The proposed method......................5
CHAPTER 2 OBJECT SEGMENTAION..................7
2.1. Background modeling......................7
2.2. Pixel classification....................13
2.3. Post-processing.........................15
2.3.1. Noise-removing...............15
2.3.2. Hole-filling.................17
2.3.3. Shadow-eliminating...........18
CHAPTER 3 Tracking...........................20
3.1 Indoor tracking -- An elevator detection system.......20
3.2 Outerdoor tracking -- A human tracking system.........26
CHAPTER 4 EXPERIMENTAL RESULTS...............35
4.1 The testing result of the elevator surveillance
system...................................35
4.2 The testing resutlt of a human tracking system
.........................................37
CHAPTER 5 CONCLUSIONS........................46
REFERENCES...................................47

[1] M. Celenk and H. Reza, “Moving Object Tracking Using
Local Windows,” Proc. IEEE International Symposium on
Intelligent Control, pp.180-185, 1989.
[2] M. Yachida,, M. Asada, and S. Tsuji, “Automatic Analysis
of Moving Images,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol.3, no.1, pp.12-19, Jan. 1981.
[3] T. Olson and F. Brill, “Moving Object Detection and Event
Recognition Algorithms for Smart Cameras,” Proc. DARPA
Image Understanding Workshop, pp. 159-175, 1997.
[4] 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.
[5] K. Rohr, “Towards Model Based Recognition of Human
Movements in Image Sequences,” Computer Vision Graphics
and Image Processing: Image Understanding, vol.59, no.1,
pp.94-115, January, 1994.
[6] S. Choi, Y. Seo, H. Kim, and K. Hong,“ Where Are the Ball
and Player? Soccer Game Analysis with Color-based
Tracking and Image Mosaic,” International Conference on
Image Analysis, pp. 83-91, 1997.
[7] D. Jang, H. Choi, “Active Models for Tracking Moving
Objects,” Pattern Recognition, vol.33, pp. 1135-1146,
2000.
[8] J. Segen and S. Pingali, “A Camera-Based System for
Tracking People in Real Time,” Proc. IEEE International
Symposium on Pattern Recognition, vol.3, pp.63-67, 1996.
[9] T. Horprasert, D. Harwood, and L.S. Davis , “A Robust
Background Subtraction and Shadow Detection ,”
Proceedings of 4th Asian Conference on Computer Vision,
Taipei, Taiwan, January 2000 .