博碩士論文 955202046 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:47 、訪客IP:3.145.2.184
姓名 林奇叡(Chi-jui Lin)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 先進安全車輛的前方與盲點視覺偵測
(Forward and Blind-spot Visual Detection for Advanced Safety Vehicles)
相關論文
★ 適用於大面積及場景轉換的視訊錯誤隱藏法★ 虛擬觸覺系統中的力回饋修正與展現
★ 多頻譜衛星影像融合與紅外線影像合成★ 腹腔鏡膽囊切除手術模擬系統
★ 飛行模擬系統中的動態載入式多重解析度地形模塑★ 以凌波為基礎的多重解析度地形模塑與貼圖
★ 多重解析度光流分析與深度計算★ 體積守恆的變形模塑應用於腹腔鏡手術模擬
★ 互動式多重解析度模型編輯技術★ 以小波轉換為基礎的多重解析度邊線追蹤技術(Wavelet-based multiresolution edge tracking for edge detection)
★ 基於二次式誤差及屬性準則的多重解析度模塑★ 以整數小波轉換及灰色理論為基礎的漸進式影像壓縮
★ 建立在動態載入多重解析度地形模塑的戰術模擬★ 以多階分割的空間關係做人臉偵測與特徵擷取
★ 以小波轉換為基礎的影像浮水印與壓縮★ 外觀守恆及視點相關的多重解析度模塑
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 在台灣,每年有超過兩千人因為交通事故而死。有鑑於此,發展車輛輔助安全駕駛的議題,也就越顯得重要。在我們的安全駕駛的研究議題上,我們架設相機在車上,再將拍攝的影像傳到電腦上執行前車或側後方車輛的偵測及追蹤,並分析前車或側後方車與己車的行車狀況,確保駕駛人於行車時的安全。
到目前為止我們的前車系統已有十項偵測及分析功能:車道線偵測、多車道估計、虛實車道線分類、己車方向估計、己車左右位置估計、偏離車道警示、前車偵測、前車距離估計、煞車燈偵測、及方向燈偵測。我們改進了其中部份功能,另外也新加入了側邊盲點的視覺偵測功能。到目前為止側邊盲點的視覺偵測已有四項偵測及分析功能:車道線偵測、虛實車道線分類、側方車輛偵測、及側方車輛距離估計。利用事先定義的車道線模組,我們尋找最符合該模組的近側車道線。並且以車底陰影及車輛左右垂直邊來偵測車體區塊,並且對車體區塊進行追蹤。
我們以多種影像;例如,晴天、陰天、及多雲的高速公路等,來測試我們演算法的偵測效能。從實驗結果顯示,我們所提出的方法可以在不同的天候狀況下,即時且快速的偵測前方與側邊車輛。前車偵測在各種天候下約有95 % 的正確率;側邊車輛偵測約有92 % 的正確率。
摘要(英) Developing real-time automotive driver assistance systems to alert drivers for possible collision with other vehicles has attracted lots of attention lately. In this study, we use cameras mounted on a vehicle to capture road scenes for forward lane detection, preceding vehicle detection, and blind-spot visual detection.
The forward visual system consists of ten functions: lane detection, multiple lane estimation, classification of solid/dashed lane marks, direction estimation, vehicle lateral offset estimation, lane departure warning, preceding vehicles detection, distance of preceding vehicle estimation, brake light detection, and turn signal detection.
The blind-spot system contains four modules: near lane mark detection, classification of solid/dashed lane marks, side vehicle detection, and distance estimation.
In the proposed system, the lane marks are detected by searching the optimal parameters of a defined lane model on the images. Preceding vehicle is detected by underneath shadow and left/right borders; then use ratio of the road width and vehicle width, symmetry, and variance to verify vehicles. The proposed vehicle detector uses template matching, symmetry, and appearance times to detect brake light, and uses template matching, position check, cluster, and frequency to detect turn signals. Side vehicle is detected by underneath shadow and left/right borders.
In the experiments, the proposed detectors were evaluated on several different weather conditions such as sunny day, misty day, dusky day, cloudy day, and dark night. From the experiment results, we find that the proposed approach can stably detect or track the lanes and vehicles in real time.
關鍵字(中) ★ 車輛偵測
★ 盲點偵測
★ 道路線偵測
★ 距離估計
關鍵字(英) ★ blind-spot detection
★ distance estimation
★ lane detection
★ vehicle detection
論文目次 摘要 ......................II
誌謝 ......................III
目錄 ......................IV
第一章 緒論 ...............一
第二章 相關研究 ...........二
第三章 前方視覺偵測 .......三
第四章 側邊盲點視覺偵測 ...五
第五章 實驗 ...............六
第六章 結論及未來工作 .....七
附錄 英文版論文 ...........八
參考文獻 [1]Armingol, J. M., A. de la Escalera, C. Hilario, J. M. Collado, J. P. Carrasco, M. J. Flores, J. M. Pastor, and J. Rodríguez, “IVVI: Intelligent vehicle based on visual information,” Robotics and Autonomous Systems, vol.55, issue 12, pp.904-916, Dec. 2007.
[2]Batavia, P. H., D. A. Pomerleau, and C. E. Thorpe, “Overtaking vehicle detection using implicit optical flow,” in Proc. IEEE Conf. on Intelligent Transportation System, Pittsburgh, PA, Nov.9-12, 1997, pp.729-734.
[3]Bertozzi, M., A. Broggi, A. Fascioli, and S. Nichele, “Stereo vision-based vehicle detection,” in Proc. IEEE Intelligent Vehicles Sym., Dearborn, MI, Oct.3-5, 2000, pp.39-44.
[4]Betke, M., E. Haritaoglu, and L. S. Davis, “Real-time multiple vehicle detection and tracking from a moving vehicle,” Machine Vision and Applications, vol.12, pp.69-83, Sep. 2000.
[5]Chen, K.-W., Monocular Computer Vision Techniques for Road and Situation Detection, Master thesis, Computer Science and Information Engineering Dept., National Central Univ., Chung-li, Taoyuan, Taiwan, 2005.
[6]Chern, M.-Y., “Development of a vehicle vision system for vehicle/lane detection on highway,” in Proc. of the 18th IPPR Conf. on Computer Vision, Graphics and Image Processing, Taipei, Taiwan, Aug.21-23, 2005, pp.803-810.
[7]Collado, J.M., C. Hilario, A. de la Escalera, and J.M. Armingol, “Model based vehicle detection for intelligent vehicles,” in Proc. IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.572-577.
[8]Guiducci, A., “Parametric model of the perspective projection of a road with applications to lane keeping and 3D road reconstruction,” Computer Vision and Image Understanding, vol.73, no.3, pp.414-427, Mar. 1999.
[9]Guiducci, A., “Camera calibration for road applications,” Computer Vision and Image Understanding, vol.79, no.2, pp.250-266, Aug. 2000.
[10]Huang, S.-S., C.-J. Chen, P.-Y. Hsiao, and L.-C. Fu, “On-board vision system for lane recognition and front-vehicle detection to enhance driver’s awareness,” in Proc. IEEE Int’l Conf. on Robotics and Automation, New Orleans, LA, Apr.26-May 1, 2004, pp.2456-2461.
[11]Huang, Y.-C. and D.-C. Tseng, “A vision-based vehicle to vehicle detection and tracking system,” in Proc. of the 18th IPPR Conf. on Computer Vision, Graphics and Image Processing, Taipei, Taiwan, Aug.21-23, 2005, pp.866-873.
[12]Jung, C. R. and C. R. Kelber, “A lane departure warning system using lateral offset with uncalibrated camera,” in Proc. IEEE Conf. on Intelligent Transportation Systems, Vienna, Austria, Sep.13-16, 2005, pp.102-107.
[13]Kate, T. K., M. B. van Leewen, S.E. Moro-Ellenberger, B. J. F. Dressen, A. H. G. Versluis, and F. C. A. Groen, “Mid-range and distant vehicle detection with a mobile camera,” in Proc. IEEE Conf. on Intelligent Vehicles, Parma, Italy, Jun. 14-17, 2004, pp.72-77.
[14]Lai, T.-C., A Real-Time Robust On-Vehicle Generic Obstacle and Lane Detection System Based on Computer Vision Technique, Master thesis, Electronic and Control Engineering Dept., National Chiao Tung Univ., Hsinchu, Taiwan, 2005.
[15]Mota, S., E. Ros, E. M. Ortigosa, and F. J. Pelayo, “Bio-inspired motion detection for blind spot overtaking monitor,” International Journal of Robotics and Automation, vol.19, no.4, pp.190-196, 2004.
[16]Reichardt, W., “Autocorrelation, a principle for evaluation of sensory information by the central nervous system,” in Sensory Communication, W. A. Rosenblith, ed., Wiley, New York, 1961, pp.303-317.
[17]Shih, H.-W. and D.-C. Tseng, "Visual detection of preceding vehicles and their brake / turn lights," in Proc. of the 19th IPPR Conf. on Computer Vision, Graphics and Image Processing, Taoyuan, Taiwan, Aug.13-15, 2006.
[18]Shyr, B.-Y., Daytime Detection of Leading and Neighboring Vehicles on Highway: A Major Capability for the Driver Assistant Vision System, Master thesis, Elect. Eng. Dept., National Chung Cheng Univ., Chia-yi, Taiwan, 2003.
[19]Tseng, D.-C., Monocular Computer Vision Aided Road Vehicle Driving for Safety, U.S. Patent, No. 6765480, July 20, 2004.
[20]Wang, C.-C., Driver Assistance System for Lane Departure Prevention and Collision Avoidance with Night Vision, Master thesis, Computer Science and Information Engineering Dept., National Taiwan Univ., Taipei, Taiwan, 2004.
[21]Wang, C.-C., C.-J. Chen, Y.-M. Chan, L.-C. Fu, and P.-Y. Hsiao, "Lane detection and vehicle recognition for driver assistance system at daytime and nighttime," Image and Recognition Magazine, vol.12, no.2, pp.4-17, 2006.
[22]Wang, J., G. Bebis, and R. Miller, “Overtaking vehicle detection using dynamic and quasi-static background modeling,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, Jun.20-26, 2005, pp.64-71.
[23]Wang, Y., E. K. Teoh, and D. Shen, “Lane detection and tracking using B-Snake,” Image and Vision Computing, vol.22, issue 4, pp.269-280, Apr. 2004.
[24]Wu, B.-F., W.-H. Chen, C.-W. Chang, C.-J. Chen, and M.-W. Chung, “A new vehicle detection with distance estimation for lane change warning systems,” in Proc. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, Jun.13-15, 2007, pp.698-703.
[25]Zielke, T., M. Brauckmann, and W. Von Seelen, "Intensity and edge-based symmetry detection with an application to car-following," CVGIP: Image Understanding, vol.58, no.2, pp.177-190, 1993.
指導教授 曾定章(Din-chang Tseng) 審核日期 2008-7-16
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明