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研究生:張凱翔
研究生(外文):Chang, Ksi-Hsiang
論文名稱:多階段性移動式違規停車偵測
論文名稱(外文):Multi-stage Mobile Parking Violation Detection
指導教授:黃經堯黃經堯引用關係
指導教授(外文):Huang, Ching-Yao
口試委員:楊人順張一介
口試委員(外文):Yang, Jen-ShuenChang, Yi-Chieh
口試日期:2018-9-20
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電子研究所
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:2018
畢業學年度:107
語文別:英文
論文頁數:32
中文關鍵詞:移動式偵測違規停車偵測
外文關鍵詞:mobile detectionparking violation detection
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本論文旨在研究運用機器學習分析行車紀錄影像並自動偵測與蒐集違規停車資訊。智慧型運輸系統(Intelligent transportation system)的目標是希望未來人們的交通系統能更加的便利與安全,有效的交通資訊蒐集便是其中一項不可或缺的功能,我們提出一種移動式偵測系統,建立在城市中原有的公車系統上,利用每一台公車當作偵測的節點,搭配行車紀錄蒐集影像並且在本地自動化運算分析,透過各個節點提供的有效資訊連接成整個城市的交通偵測系統。在本論文中,我們專注於交通中違規停車於紅黃線的案件,希望能達到即時性的自動化偵測,有別於以往提出利用靜態的攝影機偵測固定的區域,我們提出運用行駛中的行車紀錄影像做多階段性分析,實現移動式偵測違規停車,提供一個有效率的大範圍違規停車資訊蒐集方式。
Over the past decades, Intelligent Transportation System has developed in order to improve transportation more safety and mobility. One of indispensable function in Intelligent Transportation System is efficient traffic information extraction. We propose a dynamic traffic surveillance system, regarding each bus as one detection node. This traffic surveillance network would detect the whole city through efficient information collection from each mobile detection node. In this thesis, we focus on the detection of parking violation on red and yellow lane. Compare with the method using static camera to detect illegal parking region, we propose a multi-stage algorithm to analyze video from dashboard camera, and automatically detect parking violation cases in real time. The target is offering an efficient method to collect parking violation information in a wide range.
摘 要....................................iii
ABSTRACT..................................iv
Contents..................................vi
Chapter 1 Overview.......................- 1 -
1.1 Introduction.........................- 1 -
1.2 Outline..............................- 2 -
1.3 Definitions and abbreviation.........- 3 -
Chapter 2 Background knowledge...........- 4 -
2.1 Global Positioning System (GPS)......- 4 -
2.2 Support Vector Machines (SVMs).......- 5 -
2.3 Vision-based Vehicle Detection.......- 6 -
Chapter 3 Relative Work..................- 9 -
Chapter 4 Proposed Solution..............- 11 -
4.1 Scenario.............................- 11 -
4.2 Bus’s advantages.....................- 12 -
4.3 Comparison between mobile detection and static detection................................- 13 -
4.4 Feature Definitions..................- 13 -
4.5 System Architecture..................- 14 -
4.6 Implement............................- 15 -
Chapter 5 Details of Algorithms..........- 16 -
5.1 Overview of Algorithm................- 16 -
5.2 Vehicle Motion Prediction............- 17 -
5.2.1 Rerouting and buffer...............- 18 -
5.2.2 Feature transformation.............- 19 -
5.2.3 Support Vector Machines (SVMs) model prediction
.........................................- 20 -
5.2.3 Tracking...........................- 22 -
5.3 Lane Determination...................- 23 -
5.3.1 Challenges.........................- 23 -
5.3.2 Proposed method....................- 24 -
Chapter 6 Experimental RESULT AND DISCUSSION- 26 -
6.1 Experimental Environment............- 26 -
6.2 Experimental Result..................- 26 -
6.2.1 Vehicle motion prediction result...- 26 -
6.2.2 Parking violation detection result.- 27 -
6.3 Processing Time and Data Size........- 28 -
Chapter 7 Conclusion.....................- 30 -
Reference................................- 31 -
[1] Hiroshi MAKINO, Shunsuke KAMIJO, Chi-Hyun Shin and Edward Chung,” Intelligent Transport Systems (ITS) Introduction Guide,” August 2016
[2] Yangxin Lin, Ping Wang and Meng Ma,” Intelligent Transportation System(ITS): Concept, Challenge and Opportunity,” 2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity) , 17 July 2017
[3] https://ba.npa.gov.tw/npa/stmain.jsp?sys=100&kind=10&type=1&funid=q060204
[4] https://www.gps.gov/systems/gps/
[5] https://en.wikipedia.org/wiki/Support_vector_machine
[6] Sayanan Sivaraman and Mohan Manubhai Trivedi,” Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis,” IEEE Transactions on Intelligent Transportation Systems, 18 July 2013
[7] Joseph Redmon, Santosh Divvala and Ross Girshick,” You Only Look Once: Unified, Re-al-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 12 December 2016
[8] A. Vacavant, L. Tougne, T. Chateau, Special section on "Background models compari-son", Computer Vision and Image Understanding, CVIU 2014, May 2014.
[9] C. Mu, M. Xing, and P. Zhang, "Smart Detection of Vehicle in Illegal Parking Area by Fusing of Multi-features," in International Conference on Next Generation Mobile Appli-cations, Services and Technologies, pp. 388-392, 2015.
[10] W. Hassan, P. Birch, R. Young, and C. Chatwin, “Real-time occlusion tolerant detection of illegally parked vehicles,” Int. J. Control Autom. Syst., vol. 10, no. 5, pp. 972–982, 2012.
[11] Wahyono, A. Filonenko, and K.-H. Jo, “Illegally parked vehicle detection using adaptive dual background model,” in Proc. IEEE Ind. Electron. Soc. Conf., Nov. 2015, pp. 2225–2228.
[12] Wahyono and Kang-Hyun Jo,” Cumulative Dual Foreground Differences for Illegally Parked Vehicles Detection”, IEEE Transactions on Industrial Informatics, 07 February 2017
[13] Jong Taek Lee, Michael Sahngwon Ryoo and Matthew Riley,” Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation”, IEEE Transactions on Circuits and Systems for Video Technology, 07 April 2009
[14] Xuemei Xie †, Chenye Wang, Shu Chen, Guangming Shi and Zhifu Zhao,” Real-Time Illegal Parking Detection System Based on Deep Learning” the 2017 International Con-ference, June 2017
[15] http://www.cross.cz/en/products-traffic-violation
[16] https://www.allgovision.com/
[17] Diogo Carbonera Luvizon, Bogdan Tomoyuki Nassu and Rodrigo Minetto,” A Vid-eo-Based System for Vehicle Speed Measurement in Urban Roadways”, IEEE Transactions on Intelligent Transportation Systems, 26 September 2016
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