臺灣博碩士論文加值系統

隧道內交通意外往往會造成巨大災害且難以處理，因此有大量監視攝影機裝設於隧道中，可即時發現事故並監控路況。但通常並沒有足夠的人力來觀看大量的監視器畫面，使得自動化監控系統的需求增加。本論文提出一種多攝影機車輛辨識系統，利用隧道內多攝影機的監視器畫面追蹤行車在隧道內的位置。
於單一監視器畫面中，使用Haar-like特徵偵測找出車輛，並取出OpponentSIFT影像特徵。接著，本論文提出的空間時間連續關係動態規劃(S2DP)演算法，利用隧道內行車順序關係性，辨識前後兩台攝影機中所偵測到的車輛。此外亦提供兩種進階辨識方法，包含即時運算(RT)方法以及非即時加強處理(OR)。即時運算方法減少車輛配對之搜尋範圍，並快速比對兩攝影機內之車輛。而非即時方法針對空間時間連續關係動態規劃演算法中無法有效配對的行車做進一步處理。
實驗結果顯示所提出之多攝影機車輛辨識系統可得到滿意的準確程度，並優於其他相關演算法。

Surveillance cameras are widely equipped in tunnels to monitor the traffic condition and traffic safety issues. Identifying vehicles from multiple cameras within a tunnel automatically is essential to analyze traffic condition through the road. This thesis proposes a multi-camera vehicle identification system for tunnel surveillance videos.
Vehicles are detected using Haar-like feature detector and their image features are extracted using OpponentSIFT descriptor in single camera. The proposed Spatiotemporal Successive Dynamic Programming (S2DP) algorithm identifies vehicles from two cameras by considering the ordering constraint in the tunnel environment. Next, two methods Real-Time (RT) algorithm and Offline Refinement (OR) algorithm are proposed for different requirements. The RT fast identifies vehicles in real-time by searching a limited range of candidates, and the OR refines the identification result from the S2DP.
Comprehensive experiments on various datasets demonstrate the satisfactory performance of the proposed multi-camera vehicle identification methods, which outperform state-of-the-art algorithms.

Abstract (Chinese) i
Abstract (English) ii
Acknowledgements iv
Table of Contents v
List of Figures vii
List of Tables x
Chapter 1. Introduction 1
Chapter 2. Related Work 6
2.1 Video Surveillance Systems 6
2.2 Object Detection and Tracking 8
2.2.1 Object Detection 8
2.2.2 Object Tracking 10
2.3 Multi-Camera Object Identification and Tracking 10
Chapter 3. Multi-Camera Vehicle Identification 13
3.1 An Overview of the Proposed Framework 13
3.2 Vehicle Detection and Tracking in Single Camera 16
3.2.1 Vehicle Detection 16
3.2.2 Vehicle Tracking 18
3.3 Feature Extraction 19
3.3.1 Image Intensity 20
3.3.2 Color Histograms 21
3.3.3 Haar-Like Feature Vector 22
3.3.4 Keypoints Descriptors 22
3.4 Multi-Camera Vehicle Groups Matching 25
3.4.1 Spatiotemporal Successive Dynamic Programming (S2DP) 26
3.5 Real-Time and Offline Vehicle Identification 31
3.5.1 Real-Time Identification 33
3.5.2 Offline Refinement 37
Chapter 4. Experiments 39
4.1 Datasets 39
4.2 Feature Selection 41
4.3 Multi-Camera Vehicle Groups Matching 44
4.4 Real-Time and Offline Vehicle Identification 50
4.4.1 Real-Time Identification 50
4.4.2 Offline Refinement 54
4.5 Discussions 56
4.5.1 Miss-Match Penalty 56
4.5.2 More Datasets 60
Chapter 5. Conclusion and Future Work 63
Bibliography 65

B. Tian, Q. Yao, Y. Gu, K. Wang and Y. Li, "Video Processing Techniques for Traffic Flow Monitoring: A Survey," in Proc. IEEE Int'l Conf. Intelligent Transportation Systems, 2011.
M. S. Shehata, J. Cai, W. M. Badawy, T. W. Burr, M. S. Pervez, R. J. Johannesson and A. Radmanesh, "Video-Based Automatic Incident Detection for Smart Roads: The Outdoor Environmental Challenges Regarding False Alarms," IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 2, pp. 349-360, Jun. 2008.
P. Dollar, C. Wojek, B. Schiele and P. Perona, "Pedestrian Detection: An Evaluation of the State of the Art," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 743-761, Apr. 2012.
T. Gandhi and M. M. Trivedi, "Pedestrian Protection Systems: Issues, Survey, and Challenges," IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 3, pp. 413-130, Sep. 2007.
C. Rougier, J. Meunier, A. St-Arnaud and J. Rousseau, "Robust Video Surveillance for Fall Detection Based on Human Shape Deformation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 5, pp. 611-622, May 2011.
R. S. Feris, B. Siddiquie, J. Petterson, Y. Zhai, A. Datta, L. M. Brown and S. Pankanti, "Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos," IEEE Transactions on Multimedia, vol. 14, no. 1, pp. 28-42, Feb. 2012.
B. T. Morris and M. M. Trivedi, "Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2287-2301, Nov. 2012.
I. Saleemi, L. Hartung and M. Shah, "Scene Understanding by Statistical Modeling of Motion Patterns," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
R. R. Cabrera, T. Tuytelaars and L. Van Gool, "Efficient Multi-Camera Vehicle Detection, Tracking, and Identification in a Tunnel Surveillance Application," Computer Vision and Image Understanding, vol. 116, no. 6, pp. 742-753, Jun. 2012.
R. R. Cabrera, T. Tuytelaars and L. Van Gool, "Efficient Multi-Camera Detection, Tracking, and Identification Using a Shared Set of Haar-Features," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," in Proc. IEEE CS. Conf. Computer Vision and Pattern Recognition, 2001.
N. Buch, S. A. Velastin and J. Orwell, "A Review of Computer Vision Techniques for the Analysis of Urban Traffic," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 920-939, Sep. 2011.
J. Zhang, F. Y. Wang, K. Wang, W. H. Lin and C. Chen, "Data-Driven Intelligent Transportation Systems: A Survey," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624-1639, Dec. 2011.
S. Atev, G. Miller and N. P. Papanikolopoulos, "Clustering of Vehicle Trajectories," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 647-657, Sep. 2010.
M. Piccardi, "Background subtraction techniques: a review," in Proc. IEEE Int'l Conf. Systems, Man, Cybernetics, 2004.
S. Brutzer, B. Hoferlin and G. Heidemann, "Evaluation of Background Subtraction Techniques for Video Surveillance," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
C. Stauffer and W. E. L. Grimson, "Adaptive Background Mixture Models for Real-Time Tracking," in Proc. IEEE CS. Conf. Computer Vision and Pattern Recognition, 1999.
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in Proc. IEEE CS. Conf. Computer Vision and Pattern Recognition, 2005.
M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier and L. Van Gool, "Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 9, pp. 1820-1833, Sep. 2011.
C. R. Wren, A. Azarbayejani, T. Darrell and A. P. Pentland, "Pfinder: Real-Time Tracking of the Human Body," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, Jul. 1997.
M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, Feb. 2002.
X. Wang, "Intelligent Multi-Camera Video Surveillance: A Review," Pattern Recognition Letters, vol. 34, no. 1, pp. 3-19, Jan. 2013.
G. Lian, J. H. Lia, C. Y. Suen and P. Chen, "Matching of Tracked Pedestrians Across Disjoint Camera Views Using CI-DLBP," IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 7, pp. 1087-1099, Jul. 2012.
K. E. A. van de Sande, T. Gevers and C. G. M. Snoek, "Evaluating Color Descriptors for Object and Scene Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1582-1596, Sep. 2010.
D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004.
E. Rublee, V. Rabaud, K. Konolige and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in Proc. IEEE Int'l Conf. Computer Vision, 2011.
E. Rosten and T. Drummond, "Machine Learning for High-Speed Corner Detection," in Proc. European Conference on Computer Vision, 2006.
M. Calonder, V. Lepetit, C. Strecha and P. Fua, "BRIEF: Binary Robust Independent Elementary Features," in Proc. European Conference on Computer Vision, 2010.
H. W. Kuhn, "The Hungarian Method for the Assignment Problem," Naval Research Logistics Quarterly, vol. 2, pp. 83-97, 1955.
P. F. Felzenszwalb and R. Zabih, "Dynamic Programming and Graph Algorithms in Computer Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 721-740, Apr. 2011.
"Taiwan Area National Freeway Bureau, MOTC," [Online]. Available: http://www.freeway.gov.tw.