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研究生:張詠昇
研究生(外文):Yung-Sheng Chang
論文名稱:夜間交通壅塞估測之研究
論文名稱(外文):A Study on Traffic Congestion Estimation in the Nighttime
指導教授:曾逸鴻曾逸鴻引用關係
指導教授(外文):Yi-Hong Tseng
口試委員:曹偉駿林春宏
口試委員(外文):Woei-Jiunn TsaurChuen-Horng Lin
口試日期:2014-07-08
學位類別:碩士
校院名稱:大葉大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:49
中文關鍵詞:交通監控煞車燈偵測光暈變化分析夜間交通壅塞
外文關鍵詞:Traffic MonitoringBrake Light DetectionHalo change analysisNight traffic congestion
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在科技日新月異的時代,人們生活水平不斷的提昇。而近幾年國內機動車輛數目逐漸增加,交通壅塞問題更是城市居民首要克服的主要課題。近年來政府廣為提倡大眾運輸,致力於智慧型運輸系統(Intelligent Transportation Systems, ITS)的開發,期望能降低交通擁擠、提高運輸機動性。隨著交通監控系統的進步,目前的研究大多數著重在於日間交通監控的分析。因此本研究提出一種夜間即時監控交通壅塞狀況的方法,首先判定目前影像畫面的環境光源做亮度平衡及色彩調整,接著區分車輛煞車燈及其光暈,計算車輛煞車燈及其光暈的數量,並透過連續畫面分析車輛煞車燈及其光暈的變異量和變異度,進而判斷交通壅塞的程度。實驗結果在估測交通壅塞的部分,正確率能達到84.62%,表示本研究所提出的方法可用在即時穩定的ITS中。
In the era of ever-changing technology, people's living standards improved. In recent years, increasing the number of domestic motor vehicles, traffic congestion problem is the main subject of urban primary overcome. In recent years, the government is widely promote public transport, dedicated Intelligent Transportation Systems (Intelligent Transportation Systems, ITS) development, hoping to reduce congestion and improve transport mobility. With the advancement of traffic monitoring system, the present study is to analyze the most focused daytime traffic monitoring. Therefore, this study presents a real-time monitoring of traffic congestion nighttime conditions method, first determine the current ambient light image of the screen to make adjustments brightness and color balance, and then distinguish the vehicle brake lights and glow, calculate the number of vehicle brake lights and glow, and analysis of variance and variability of the vehicle brake lights and glow through continuous screen, and then determine the degree of traffic congestion. Experimental results in some estimates traffic congestion, the correct rate can reach 84.62%, which means that the proposed method can be used in real-time stability in ITS.
內容目錄
中文摘要 iii
英文摘要 iv
致謝辭 v
內容目錄 vi
圖目錄 viii
表目錄 x
第一章 緒論 1
第一節 研究背景 2
第二節 研究動機 4
第三節 研究目的 4
第四節 系統流程 5
第五節 研究範圍與限制 7
第六節 論文架構 7
第二章 文獻探討 8
第一節 智慧型運輸系統 8
第二節 前景物體偵測 9
第三節 車流量分析與壅塞判定 11
第三章 視訊畫面前處理 12
第一節 環境光源型態判定 12
第二節 亮度平衡暨色彩調整 17
第四章 交通壅塞情況估測 19
第一節 煞車燈及其光暈之分類與偵測 19
第二節 壅塞情況估測 23
第五章 實驗結果與分析 30
第一節 實驗結果 30
第二節 錯誤分析 33
第六章 結論 35
參考文獻 36

參考文獻
一、中文部分
[1] 台灣地區機動車登記數量資料來源:交通部統計處(2014), [線上資料],來源:http://stat.motc.gov.tw/mocdb/stmain.
jsp?sys=100.

[2] 林明宏, 曾逸鴻, "偵測移動車頭燈以進行夜間車流分析," 第十四屆電子化企業經營管理理論暨實務研討會,2013.

[3] 交通部台灣區國道高速公路局(2007),智慧型運輸系統ITS簡介[線上資料]來源:http://www.freeway.gov.tw/Publish.aspx? cnid=1556.
二、英文部分
[4] Kong, J., Zheng, Y., Lu, Y. and Zhang, B. "A novel background extraction and updating algorithm for vehicle detection and tracking," Proc. IEEE Int. Conf. Fuzz. Syst. Knowl. Discovery, pp.464 -468, 2007.

[5] Tsai, L.-W., Hsieh, J.-W., and Fan, K.-C. "Vehicle detection using normalized color and edge map", IEEE Trans. Image Process., 16(3), pp.850 -864, 2007.
[6] Davis, J. W., and Sharma, V. "Background-subtraction using contour-based fusion of thermal and visible imagery," Computer Vision and Image Understanding, 106(2-3), pp.162-182, 2007.

[7] Zhang, R., Zhang, S., and Yu, S. "Moving objects detection method based on brightness distortion and chromaticity distortion," IEEE Transactions on Consumer Electronics, 53(3), pp.1177-1185, 2007.

[8] Carmona, E. J., Martınez-Cantos, J., and Mira, J. "A new video segmentation method of moving objects based on blob-level knowledge," Pattern Recognition Letters, 29(3), pp.272-285, 2008.

[9] Jung, C. R. "Efficient background subtraction and shadow removal for monochromatic video sequences," IEEE Transactions on Multimedia, 11(3), pp.571-577, 2009.

[10] Bugeau, A., and Perez, P. "Detection and segmentation of moving objects in complex scenes," Computer Vision and Image Understanding, 113(4), pp.459-476, 2009.

[11] Zhang, J., and Gong, S. "People detection in low-resolution video with non-stationary background," Image and Vision Computing, 27(4), pp.437-443, 2009.

[12] Gao, X., Yang, Y., Tao, D., and Li, X. "Discriminative optical flow tensor for video semantic analysis," Computer Vision and Image Understanding, 113(3), pp.372-383, 2009.

[13] Li, B. Y., Tian, B. Li., and Yao, Q. M. "Vehicle detection based on the and-or graph for congested traffic conditions, " accepted, 14(5), pp. 984-993, 2013

[14] Liu, Junwei., and Luo, Shaokai. "A novel image segmentation technology in intelligent traffic light control systems," Consumer Electronics, Communications and Networks (CECNet), 2013 3rd International Conference on, pp. 26-29, 2013.

[15] Chen, T. H., Chen, J. L., Chen, C. H., and Chang, C. M. "Vehicle detection and counting by using headlight information in the dark environment," In Intelligent Information Hiding and Multimedia Signal Processing, 2007, IIHMSP 2007, Third International Conference on, 2(1), pp 519-522, Nov. 2007.

[16] Robert, K. "Night-time traffic surveillance: A robust framework for multivehicle detection, classification and tracking," Proc. IEEE Conf. Adv. Video Signal Based Surv, pp.1 -6, 2009.

[17] Chen, Y. L., Wu, B. F., and Fan, C. J. "Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance," Proc. IEEE Int. Conf. SMC, pp.3452 -3458, 2009.

[18] Zhang, W., Wu, Q. M. J., Wang, G., and You, X. "Tracking and pairing vehicle headlight in night scenes," IEEE Trans Intell Trans Sys 13(1), pp.140–153, 2012.

[19]Thammakaroon, P., and Tangamchit, P. "Predictive brake warning at night using taillight characteristic," Proc. IEEE International Symposium on Industrial Electronics, 2009.

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