自動感興趣區域切割及遠距交通影像中的軌跡分析

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姓名

楊鈺瀅(Yu-Ying Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

自動感興趣區域切割及遠距交通影像中的軌跡分析
(Automatic Region of Interest Segmentation and Trajectory analysis in Far Distance Traffic Surveillance)

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摘要(中)

考量到維護安全及秩序的目的，基於影像資訊發展的智慧型監控系統成為熱門的研究題目。過去的監控式攝影機需要人力操作，透過人為監控，判斷是否有異常狀況發生，才能進一步針對特定狀況作處理。經歷數十年來的發展，運用圖形識別及影像處理技術的智慧型監控系統已可取代傳統的人力操作，應用於不同的環境中。其中，基於流量監控或異常路況偵測等不同目的之智慧型監控系統，廣泛地被應用於各國的重要交通路網。多數的智慧型監控系統，著重於行車狀況的監控。本研究希望能藉由監視影像的分析，自動取得交通場景的資訊。由位置較高的攝影機，取得特定路段的影像。取這段期間內行經此路段的車輛，其規律的移動資訊作為分析的對象，進而獲得此路段的道路資訊。後續更可利用所取得的道路資訊，更新導航軟體的路網，或者政府機關的即時路況資訊等等。
研究中所使用到的監視影像，拍攝的位置較高，距路面較遠。在拍攝位置較遠的狀況下，所得到的監視影像不同於一般的車流監視影像，有以下特性：拍攝範圍較廣，可涵蓋多條路線；車體較小，且在畫面中呈現為長方形色塊，較無立體感；雜訊較多，如行人、飛鳥等生物在經過畫面時面積極小，並有規律性地移動，干擾軌跡擷取及後續分析。
本研究首先利用連續影像相減法，取得畫面中有移動的部分，再對這些區域擷取特徵，再進行特徵點的追蹤，取得軌跡。將追蹤所取得的軌跡作分群，畫面中的軌跡根據所在位置及方向資訊，被分入不同的群聚中。以軌跡位置與群集中心所算出的變異數大小作評估，希望能取得分布最為密集的分群結果。分別算出各條軌跡的主要前進方向，並針對各個群聚，將主方向不同的軌跡分割出來。分割後，依據各個群聚中軌跡取樣點位置的累計直方圖，推測車道的分隔線，再分割出不同車道的群聚。最後分析各個群聚周邊的相鄰群聚，依據相鄰群聚的位置和主方向的資訊，連結各個路線，最後得到畫面中的路向和路口資訊。

摘要(英)

For traffic safety and efficiency, the surveillance system based on image analysis has become popular research project in recent years. Intelligent surveillance system using pattern recognition and image processing technologies have been developed and improved intensively in this decade. In the thesis, we propose a method to automatically segment the regions of interest in the surveillance videos via analysis of vehicle trajectories. The system aims at dealing with wide-range surveillance, which is an important complement to ground-plane surveillance. The traffic scenes in experimental videos are taken from high buildings. Therefore, the vehicles in the scene are not stereoscopic. The pedestrians are just like black spots, and there are many noises in the scenes.
First, we do inter-frame differencing and dilation to get the motion region. Then, we perform tracking using the ORB feature, HOG and the color histogram in the motion region. And the tracking results are stored as the trajectories. Afterwards, we use spectral method to automatically determine the number of clusters, and use K-means with modified Hausdorff distance to cluster the trajectories. Then the clusters are segmented with the orientations of trajectories. Assuming that most of vehicles move along the lanes, and lane changing seldom appears. We apply the point rotation and projection to get the valley in the distribution range of each cluster. The position of valley implies the position of lane. After the segmentation of lanes, we merge the overlapping cluster. Finally, to get the correct road map, we do connection with neighbor cluster and orientation. We conduct experiments with different challenging surveillance scenes to validate the proposed method.

關鍵字(中)

★ 軌跡分群
★ 豪斯多夫距離
★ 路面分析
★ 感興趣區域切割
★ 智慧型監控系統

關鍵字(英)

★ Trajectory Clustering
★ Modified Hausdorff Distance
★ Route Analysis
★ Region of Interest Segmentation
★ Intelligent Surveillance System

論文目次

摘要 i
ABSTRACT ii
目錄 iii
圖目錄 v
表目錄 viii
第一章緒論 1
1.1 研究動機 1
1.2 文獻探討 2
1.3 系統流程 6
1.4 論文架構 7
第二章追蹤 8
2.1 特徵 8
2.1.1 Oriented FAST and Rotated BRIEF (ORB) 8
2.1.2 Histograms of oriented gradients (HOG) 11
2.1.3 Color Histogram 12
2.2 追蹤 12
第三章軌跡分群及主要道路分析 16
3.1 群聚 16
3.1.1 距離度量 16
3.1.2 相似性度量 17
3.1.3 群聚方法 18
3.2 群集分割 22
3.2.1 Segmentation with Orientation 23
3.2.2 Segmentation with Projection 25
3.2.3 Cluster Merging 30
3.3 群集連接 32
3.3.1 Cluster Connection with Neighbor 32
3.3.2 Route Connection with Neighbor 36
第四章實驗結果 38
4.1 實驗場景 38
4.2 實驗結果及數據分析 44
4.2.1 追蹤 44
4.2.2 Clustering 45
4.2.3 Analysis 48
4.2.4評估指標 54
4.2.3結果評估 - 感興趣區域(Region of Interest) 55
4.2.4結果評估 - 路線 57
第五章結論與未來工作 62
參考文獻 63

參考文獻

[1] S. Atev, G. Miller, and N. Papanikolopoulos, “Clustering of Vehicle Trajectories,” IEEE Transactions on Intelligent Transportation Systems, 11(3):647–657, Sept. 2010.
[2] D. Hamad, P. Biela, “Introduction to Spectral Clustering," Information and Communication Technologies: From Theory to Application, 3rd International Conference on. IEEE, pp. 1-6, April 2008.
[3] Andrew Y. Ng, Michael I. Jordan, Yair Weiss, “On Spectral Clustering: Analysis and an Algorithm,” Advances in Neural Information Processing Systems, 849-856, 2001.
[4] Lihi Zelnik-manor, Pietro Perona, “Self-Tuning Spectral Clustering,” Advances in Neural Information Processing Systems 17, 1601-1608, 2004.
[5] Brendan Tran Morris , Mohan Trivedi, “Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11):2287-2301, Nov. 2011.
[6] Weiming Hu, Dan Xie, and Tieniu Tan, “A Hierarchical Self-Organizing Approach for Learning the Patterns of Motion Trajectories,” IEEE Transactions on Neural Networks, 15(1):135-144, Jan. 2004.
[7] Brendan Morris , Mohan Trivedi, “Unsupervised Learning of Motion Patterns of Rear Surrounding Vehicles,” IEEE Transactions on Vehicular Electronics and Safety, 80-85, Nov. 2009.
[8] Weiming Hu, Dan Xie, Tieniu Tan, and Steve Maybank, “Learning Activity Patterns Using Fuzzy Self-Organizing Neural Network,” IEEE Transactions on System, Man, and Cybernetics, 34(3):1618-1626, June 2004.
[9] Weiming Hu , Xuejuan Xiao , Zhouyu Fu , Dan Xie , Tieniu Tan , Steve Maybank, “A System for Learning Statistical Motion Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9):1450-1464, Sept. 2006.
[10] Brendan Tran Morris, Mohan Manubhai Trivedi, “Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video,” IEEE Transactions on Intelligent Transportation Systems, 9(3):425-437, Sept. 2008.
[11] Chris Stauffer , W. Eric L. Grimson, “Learning Patterns of Activity Using Real-Time Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):747-757, Aug. 2000.
[12] Zhouyu Fu, Weiming Hu, Tieniu Tan, “Similarity-Based Vehicle Trajectory Clustering and Anomaly Detection,” IEEE International Conference on Image Processing, 2:602-605, Sept. 2005.
[13] Imran N. Junejo, Omar Javed and Mubarak Shah, “Multi feature path modeling for video surveillance,” Proceedings of the 17th International Conference on Pattern Recognition, 2:716-719, Aug. 2004.
[14] Brendan T. Morris and Mohan M. Trivedi, “Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis,” IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 154-161, Sept. 2008.
[15] Eran Swears, Anthony Hoogs and A.G. Amitha Perera, “Learning Motion Patterns in Surveillance Video Using HMM Clustering,” IEEE Workshop on Motion and video Computing, 1-8, Jan. 2008.
[16] C. Piciarelli , G. L. Foresti, “On-line Trajectory Clustering for Anomalous Events Detection,” Proceedings of the Pattern Recognition, 1835-1842, Nov. 2006.
[17] Fatih Porikli, “Learning Object Trajectory Patterns by Spectral Clustering,” IEEE International Conference on Multimedia and Expo, 2:1171-1174, June 2004.
[18] Brendan Morris and Mohan Trivedi, “Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation,” IEEE Conference on Computer Vision and Pattern Recognition, 312-319, June 2009.
[19] Brendan Tran Morris and Mohan Manubhai Trivedi, “A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance,” IEEE Transactions on Circuits and Systems for Video Technology, 18(8):1114-1127, Aug. 2008.
[20] Zhang Zhang, Kaiqi Huang, Tieniu Tan, “Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes,” Proceedings of the 17th International Conference on Pattern Recognition, 3:1135-1138, Aug. 2006.
[21] Marie-Pierre Dubuisson and Anil K. Jain, “A Modified Hausdorff Distance for Object Matching,” Proceedings of the 12th IAPR International Conference on Computer Vision & Image Processing, 1:566-568, Oct. 1994.
[22] Daniel P. Huttenlocher, Gregory A. Klanderman, and William J. Rucklidge, “Comparing Images Using the Hausdorff Distance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850-863, Sep. 1993.
[23] Jiangung Lou, Qifeng Liu, Tieniu Tan and Weirning Hu,”Semantic Interpretation of Object Activities in a Surveillance System,” Proceedings of the 16th International Conference on Pattern Recognition, 3:777-780, 2002.
[24] Stefan Atev, Osama Masoud, and Nikos Papanikolopoulos, “Learning Traffic Patterns at Intersections by Spectral Clustering of Motion Trajectories,” IEEE/RSJ International Conference on Intelligent Robots and Systems, 4851-4856 Oct. 2006.
[25] Yuanfeng Yang, Zhiming Cui, Jian Wu, Guangming Zhang, Xuefeng Xian, “Trajectory Analysis Using Spectral Clustering and Sequence Pattern Mining,” Journal of Computational Information Systems, 8(6):2637-2645, March 2012.
[26] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski, “ORB: an efﬁcient alternative to SIFT or SURF,” IEEE International Conference on Computer Vision, 2564-2571, Nov. 2011.
[27] Herbert Bay, Tinne Tuytelaars, Luc Van Gool, “SURF: Speeded Up Robust Features,” Journal of Computer Vision and Image Understanding, 110(3):346-359, June 2008.
[28] Michael Calonder, Vincent Lepetit, Pascal Fua, “BRIEF: Binary Robust Independent Elementary Features,” Proceedings of the 11th European conference on Computer vision, 778-792, 2010.
[29] Chris Harris , Mike Stephens, “A Combined Corner and Edge Detector,” Proceedings of the Fourth Alvey Vision Conference, 147-151, 1988.
[30] Paul L. Rosin, “Measuring Corner Properties,” Journal of Computer Vision and Image Understanding, 73(2):291-307, Feb. 1999.
[31] Edward Rosten , Tom Drummond, “Machine Learning for High-Speed Corner Detection,” Proceedings of the 9th European conference on Computer Vision, 430-443, 2006.
[32] Marius Muja and David G. Lowe, “Fast Matching of Binary Features,” Proceedings of the Ninth Conference on Computer and Robot Vision, 404-410, 2012
[33] Marina Meila, “Comparing clusterings--an information based distance,” Journal of Multivariate Analysis, 98(5):873-895, 2007.
[34] Ondrej Miksik, Krystian Mikolajczyk, “Evaluation of Local Detectors and Descriptors for Fast Feature Matching,” International Conference on Pattern Recognition, 2681-2684, Nov. 2012.
[35] Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1:886-893, June 2005.
[36] Stefan Emilov Atev, Using Asymmetry in the Spectral Clustering of Trajectories, University of Minnesota Minneapolis, USA, 2011.
[37] Jia Wen, Chao Li, Zhang Xiong, “Behavior pattern extraction by trajectory analysis,” Journal of Frontiers of Computer Science in China, 5(1):37-44, March 2011.
[38] Tianzhu Zhang, Hanqing Lu, Stan Z. Li, “Learning Semantic Scene Models by Object Classification and Trajectory Clustering,” IEEE Conference on Computer Vision and Pattern Recognition, 1940-1947, June 2009.
[39] Nadeem Anjum and Andrea Cavallaro, “Multifeature Object Trajectory Clustering for Video Analysis,” IEEE Transactions on Circuits and Systems for Video Technology, 18(11):1555-1564, Nov. 2008.
[40] Ko-Cheung Hui and Wan-Chi Siu, “Extended Analysis of Motion-Compensated Frame Difference for Block-Based Motion Prediction Error,” IEEE Conference on Image Processing, 1232-1245, 2007.

指導教授

鄭旭詠(Hsu-Yung Cheng)

審核日期

2013-6-28

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