簡易檢索 / 詳目顯示

回結果列表
研究生： 陳彥儀
Yen-Yi Chen
論文名稱： 公車專用道之監視系統
A Restricted Bus-lane Monitoring System
指導教授： 陳世旺
Chen, Sei-Wang
學位類別： 碩士
Master
系所名稱： 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 中文
論文頁數： 71
中文關鍵詞： local optimal thresholding 、深色區域 、亮部區域 、occlusion 、候選車輛 、deformable model 、攝影機校正
英文關鍵詞： local optimal thresholding, dark regions, bright regions, occlusion, vehicle candidates, deformable model, camera calibration
論文種類： 學術論文
相關次數： 點閱：75  下載：2 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 許多城市均有設置公車專用道，它使得公車線路更快且更可靠，然而若有未經授權的車輛駛入公車專用道，將使得公車專用道的好處無法完全實現。本系統主要的目的就是提供一個自動化的系統去偵測公車專用道上非法闖入的車輛，內容大致上分為兩部分：車輛偵測以及非公車辨識。首先做車輛偵測，我們利用local optimal thresholding的方法找出兩種可靠的區域特徵，分別為dark和bright regions，由於區域特徵為局部性特徵，較不受occlusion的影響，又每一輛車都有dark和bright regions，我們可以藉由合併dark和bright regions來偵測車輛。之後對於每個前景物判斷其是否有occlusion的情形，若有則進行切割。當所有前景物都進行切割或不切割的處理之後，得到的每一個object即視為一台車輛。接著在非公車偵測中，對每一台車輛套用一個deformable model，依據在camera calibration中得到的攝影機參數，換算出每一車輛在真實世界的長、寬、高，之後便可辨認出公車專用道上的車輛是否為公車，若為非公車，則須將之標示出來。

    This paper presents a system for monitoring a bus lane to ensure that it remains effective for improving mass transportation time. The system detects unauthorized vehicles driving in the bus lane. Processing is done by two major steps: vehicle location and non-bus detection. In vehicle location, we search for dark and bright regions in the input image. Dark and bright regions are next paired if they comply with predefined criteria. Vehicle candidates are hen formed from paired dark-bright regions. In non-bus detection, for each vehicle candidate we first examine whether or not occlusions occur within the candidate. Region segmentation will be carried out if occlusions are detected. Thereafter, for each individual region we fit with a generalized deformable model. Bus and non-bus taxonomy is accomplished primarily on the basis of the size of the resultant model, which is a function of image row determined during camera calibration.

    目 錄 附表目錄　　　　　　　　　　　　　 　　　　　　　　　 　 vii 附圖目錄　　　　　　　　　　　　　　　　　　 　　　　　　 viii 第一章 簡介　　　　　　　　　　　　　　　　　　 　　　　 1-1 1.1 研究動機和目的……………………………………………………1-1 1.2 文獻探討……………………………………………………………1-4 1.3 論文架構……………………………………………………………1-7 第二章 公車專用道之監視系統　　　　　　　　　　　　　 2-1 2.1 系統設置……………………………………………………………2-1 2.2 系統運作……………………………………………………………2-2 2.2.1 前處理……………………………………………………………2-3 2.2.2 車輛定位…………………………………………………………2-4 2.2.3 非公車偵測………………………………………………………2-5 第三章 前處理 3-1 3.1 背景模型建立………………………………………………………3-1 3.2 攝影機校正…………………………………………………………3-2 第四章 車輛定位 4-1 4.1 Local optimal thresholding……………………………4-3 4.2 合併規則…………………………………………………………4-12 第五章 非公車偵測 5-1 5.1 判斷是否有occlusion的情形……………………………………5-3 5.2 切割…………………………………………………………………5-5 5.3 Vehicle modeling………………………………………………5-7 5.4 公車與非公車之分辨………………………………………………5-12 第六章 實驗結果 6-1 6.1 攝影機校正之結果…………………………………………………6-1 6.2 車輛定位及非公車偵測的結果……………………………………6-2 6.3 討論…………………………………………………………………6-7 第七章 結論及未來方向 7-1 7.1 結論…………………………………………………………………7-1 7.2 未來發展……………………………………………………………7-2 參考著作 參-1 附表目錄 表5.1 車輛分辨的依據………………………………………………………5-12 表6.1 求得之攝影機參數………………………………………………………6-1 表6.2 h…………………………………………………………………………6-10 附圖目錄 第一章 圖1.1 倫敦的公車專用道攝影機………………………………………………1-3 第二章 圖2.1 系統設置狀況……………………………………………………………2-1 圖2.2 交通影像…………………………………………………………………2-2 圖2.3 公車專用道監視系統之流程圖…………………………………………2-3 圖2.4 攝影機視線與特徵偵測之關係圖………………………………………2-5 圖2.5 Generalized model…………………………………………………2-6 第三章 圖3.1 攝影機模型………………………………………………………………3-3 圖3.2 矩形ABCD…………………………………………………………………3-4 圖3.3 攝影機校正之俯瞰示意圖………………………………………………3-4 第四章 圖4.1 車輛定位的流程圖………………………………………………………4-1 圖4.2 輸入影像…………………………………………………………………4-2 圖4.3 Example for illustrating Local Optimum Thresholding4-5 圖4.4 α and β of seed region Pk………………………………………4-6 圖4.5 γ of seed region Pk………………………………………………4-6 圖4.6 反轉的前景影像…………………………………………………………4-9 圖4.7 Construct Sp………………………………………………………4-10 圖4.8 initial seed region variation rate Gi…………………4-11 圖4.9 example of construct Gi………………………………………4-11 圖4.10 dark and bright regions………………………………………4-12 圖4.11 候選車輛………………………………………………………………4-13 第五章 圖5.1 非公車偵測之流程圖……………………………………………………5-1 圖5.2 前景物與其內所包含之候選車輛………………………………………5-4 圖5.3 vehicle model of occluded and unoccluded cases………5-5 圖5.4 輸入影像…………………………………………………………………5-6 圖5.5 two-vehicle occluded……………………………………………5-6 圖5.6 three-vehicle occluded…………………………………………5-7 圖5.7 Generalized deformable model…………………………………5-7 圖5.8 fitting of deformable model onto a vechicle…………5-9 圖5.9 model fitting………………………………………………………5-10 圖5.10 model fitting……………………………………………………5-10 圖5.11車輛modeling的結果………………………………………………5-11 圖5.12 Result of Bus/Non-bus taxonomy…………………………5.12 第六章 圖6.1 交通影像…………………………………………………………………6-1 圖6.2 前景物……………………………………………………………………6-7 圖6.3 dark and bright regions………………………………………6-7 圖6.4 dark regions…………………………………………………………6-8 圖6.5 物體與攝影機之距離遠近與物體在影像中的大小關係示意圖………6-9 圖6.6 將影像分為a、b、c、d四個部分……………………………………6-10 圖6.7 dark regions of bus……………………………………………6-11 圖6.8 dark region…………………………………………………………6-11

    [Cha93] Charkari, N.M., Mori, H., “A new approach for real time moving vehicle detection,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, pp. 273 – 278, 1993.

    [Fun03] Fung George S. K., Yung Nelson H. C., Pang Grantham K. H., “Camera calibration from road lane markings,” Opt. Eng. SPIE, vol. 42, no. 10, pp. 2976–2977, Oct. 2003.

    [Gup02] Gupte, S., Masoud, O., Martin, R.F.K., Papanikolopoulos, N.P., “Detection and classification of vehicles ,“ IEEE Transactions on Intelligent Transportation Systems, Vol. 3, pp. 37 – 47, 2002.

    [Lai01] Lai, A.H.S., Fung, G.S.K., Yung, N.H.C., “Vehicle type classification from visual-based dimension estimation,” Proceedings of Intelligent Transportation Systems, pp. 201 – 206, 2001.

    [Lee06] Lee Deaho, Park Youngtae, “Robust vehicle detection based on shadow classification,” IEEE Conference on International Pattern Recognition , Vol. 3, pp. 1167 – 1170, 2006.

    [Lin09] Lin Shanming, Tang Jun, Zhang Xuewu, Lv Yanyun, “Research on traffic moving object detection, tracking and track-generating” IEEE International Conference on Automation and Logistics, pp. 783 – 788, 2009.

    [Mor94] Mori, H., Charkari, N.M., Matsushita, T., ”On-line vehicle and pedestrian detections based on sign pattern,” IEEE Transactions on Industrial Electronics, Vol. 41,pp. 384 – 391, 1994.

    [Mos07] Mosabbeb, E.A., Sadeghi, M., Fathy, M., Bahekmat, M., “A low-cost strong shadow-based segmentation approach for vehicle tracking in congested traffic scenes,” International Conference on Machine Vision, pp. 147 – 152, 2007.

    [Pal93] Nikhil R. Pal, Sankar K. Pal, “A review on image segmentation techniques,” Pattern Recognition Society, Vol. 26, pp. 1277– 1294, 1993.

    [Pan04] Pang, C.C.C., Lam, W.W.L.; Yung, N.H.C., “A novel method for resolving vehicle occlusion in a monocular traffic-image sequence,” IEEE Transactions on Intelligent Transportation Systems, Vol. 5, pp. 129 – 141, 2004.

    [Pan07-1] Pang, C.C.C., Tan Zhigang, Yung, N.H.C., “A methodology for resolving severely occluded vehicles based on component-based multi-resolution relational graph matching,” International Conference on Machine Vision, pp. 141 – 146, 2007.

    [Pan07-2] Pang, C.C.C., Lam, W.W.L., Yung, N.H.C., “A Method for Vehicle Count in the Presence of Multiple-Vehicle Occlusions in Traffic Images,” IEEE Transactions on Intelligent Transportation Systems, Vol. 8, pp. 441 – 459,2007.

    [Par01] Park Youngtae, “Shape-resolving local thresholding for object detection,” Journal, Pattern Recognition Letters , 22(8), south korea, PP. 883–890, 2001.

    [Sad06] Sadeghi, M., Fathy, M.,”A Low-Cost Occlusion Handling Using a Novel Feature in Congested Traffic Images,” IEEE Conference on Intelligent Transportation Systems, pp. 522 – 527, 2006.

    [Wu08] Wu Wei Zhang, Q.M.J., Xiaokang Yang, Xiangzhong Fang, “Multilevel Framework to Detect and Handle Vehicle Occlusion,” IEEE Transactions on Intelligent Transportation Systems, Vol. 9, pp. 161 – 174, 2008.

    下載圖示
    QR CODE