跳到主要內容

臺灣博碩士論文加值系統

(44.200.194.255) 您好!臺灣時間:2024/07/18 13:22
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:劉育均
研究生(外文):Yuh-Jiun Liu
論文名稱:車道偏移與障礙物偵測辨識技術研究
指導教授:瞿忠正瞿忠正引用關係
學位類別:碩士
校院名稱:國防大學理工學院
系所名稱:電子工程碩士班
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:2009
畢業學年度:98
語文別:中文
中文關鍵詞:車道偏移駕駛輔助系統障礙物偵測立體視覺立體視差障礙物偵測計算機立體視覺
外文關鍵詞:Synchronous stereovision systemAsynchronous stereovision systemDisparityObstacle detection
相關次數:
  • 被引用被引用:0
  • 點閱點閱:269
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
日益繁忙的社會,交通運輸事業越顯複雜,人們對於交通安全的需求也越來越重視。電腦視覺及影像處理技術的研究也越來越成熟,已被廣泛考慮運用在自動駕駛、輔助駕駛、車輛監控…等交通運輸的議題上。本文研究一套應用立體視覺影像處理技術之車道偏移與障礙物偵測辨識技術即時偵測系統。適用於行車載具之障礙物偵測,其能於車輛行進期間,藉由架設於前座擋風玻璃之雙攝影機所擷取輸入之影像,針對車輛前方道路中的車道線和汽、機車等障礙物體,能即時的自動偵測出前方的車道線段與障礙物,並計算出車道線段長度與障礙物的相對距離,依據使用者的預警距離設定,當車道線段偏移或障礙物距離低於所設定的預警距離時,系統可以透過聲音與影像作警示,主動提醒駕駛人採行因應。
行車期間由於攝影機拍攝輸出的影像複雜且多變化,其動態變動因素控制不易,常因外在的天候、地理環境、道路平面、交通狀況,及自身車輛的輪胎氣壓、左右邊承載重量差異或引擎運轉,產生不可抗拒的震動與傾斜,導致輸入影像的攝影機架設相關參數產生持續的變化,因此以單鏡頭為基礎的立體視覺系統,無法提供穩定且正確的座標系統,作為車道偏移與障礙物距離轉換計算的基礎,所以會產生距離計算的誤差。為了能適應各式車輛平台系統所遭遇的各種可能震動與傾斜,所以本研究將利用雙鏡頭立體視覺之特性,建構出一套高穩定性且具備立體視覺之車道偏移與障礙物自動即時偵測系統。
傳統偵測道路線段的方法,通常是針對影像中的單一像素點(Pixel)或是單一掃瞄線(Scan Line)來進行決策與判斷,或將像素點之間的關連性建立起來,建立道路模型(Road Model)配合像素強度(Pixel Intensity)或邊界強度的累積量加以搜尋,來達到偵測道路線的目的,此部分不同於舊式的線段偵測方法,我們利用了立體視覺的優勢,實現即時的車道偏移與障礙物偵測。本研究將所需的影像處理程序利用所提出的演算法化繁為簡,利用一對低價位的黑白攝影機於車載具中,將擷取的影像存入電腦,利用邊緣偵測得到的資訊定位出道路線段及前方汽車位置,以立體視覺方法,實施快速比對搜尋運算求得視差值,由視差值換算出道路線段的長度及寬度與前方障礙物之距離。本系統經過初步實作後,驗證本系統的演算法是值得繼續延伸發展,針對即時車道偏移與障礙物偵測辨識成效良好。
This paper describes a multi-resolution stereovision system for detecting obstacles in advanced safety vehicles (ASVs). The two asynchronous CMOS cameras in the proposed system are mounted on a platform that can be easily clamped to the rearview mirror of a vehicle for detecting obstacles or vehicles ahead. The asynchronous binocular platform provides a small low-cost obstacle detection system for practical ASVs that is easy to set up. The system uses a stereovision obstacle detection algorithm for real-time matching because the exposure times of the CMOS cameras are not synchronous. The algorithm uses a line segment matching module to match the extreme points of the horizontal and vertical edge segments at different resolutions to decrease the search area and computing complexity. As the distance of each matched segment can be calculated from the disparity value, each obstacle can be detected by clustering the segments that have similar distances in an obstacle searching and distance estimation module. The system was evaluated using static and dynamic analyses. Experimental results show that the proposed system can robustly and accurately detect obstacles or other vehicles in real time under different illumination and road conditions.
[1] Bertozzi, M., and Broggi, A., “GOLD: A Parallel Real-time Stereo Vision System for Generic Obstacle and Lane Detection,” IEEE Transactions on Image Processing, Vol. 7, No. 1, pp. 62-81, Jan. 1998.
[2] Kluge, K., and Lakshmanan, S., “A Deformable-Template Approach to Lane Detection,” IEEE Intelligent Vehicles Symposium, pp.54- 59, September, 1995.
[3] Betke, M., Haritaoglu, E., and Davis, L. S., “Multiple Vehicle Detection and Tracking in Hard Real-time,” IEEE Intelligent Vehicles Symposium, pp. 351-356, Sep. 1996.
[4] Betke, M., Haritaoglu, E., and Davis, L. S., “Real-time Multiple Vehicle Detection and Tracking from a Moving Vehicle,” Machine Vision and Applications, Vol. 12, pp. 69-83, Sep. 2000.
[5] Xu, L., Oja, E., and Kultanen, P., “A New Curve Detection Method: Randomized Hough Transform (RHT),” Pattern Recognition Letters, pp. 331-338, 1990.
[6] McLaughlin, R. A., “Randomized Hough Transform: Better Ellipse Detection,” IEEE TENCON-Digital Signal Processing Applications, pp.409-414, 1996.
[7] Charkari, N. M., and Mori, H., “A New Approach for Real-time Moving Vehicle Detection,” IEEE Intelligent Robots and Systems, Yamanashi, Japan, Vol. 1, pp. 273-278, Jul. 1993.
[8] Mori, H., Charkari, N. M., and Matsushita, T., “On-line Vehicle and Pedestrian Detection based on Sign Pattern,” IEEE Transactions on Industrial Electronics, Vol. 41, No. 4, pp. 384-391, 1994.
[9] Graefe, V., and Efenberger, W., “A Novel Approach for the Detection of Vehicles on Freeways by Real-time Vision,” IEEE Intelligent Vehicles Symposium, Tokyo, Japan, pp. 363-368, Sep. 1996.
[10] Du, Y., and Papanikolopoulos, N. P., “Real-time Vehicle Following Through a Novel Symmetry-based Approach,” IEEE International conference on Robotics and Automation, Vol. 4, pp. 3160-3165, Apr. 1997.
[11] Labayrade, R., Royere, C., Gruyer, D., and Aubert, D., “Experimental Assessment of the Rescue Collision-mitigation System,” IEEE Transactions on Vehicular Technology, Vol. 56, pp. 89-102, Jan. 2007.
[12] Labayrade, R., Royere, C., Gruyer, D., and Aubert, D., “Detection of Visibility Conditions through Use of Onboard Cameras,” IEEE Intelligent Vehicles Symposium, Versailles, France, pp. 193-198, Jun. 2005.
[13] Labayrade, R., and Aubert, D., “A Single Framework for Vehicle Roll, Pitch, Yaw Estimation and Obstacles Detection by Stereovision,” IEEE Intelligent Vehicles Symposium, Versailles, France, pp. 31-36, Jun. 2003.
[14] Labayrade, R., Aubert, D., and Tarel, J. P., “Real-time Obstacle Detection on Non-flat Road Geometry through ‘V-disparity’ representation,” IEEE Intelligent Vehicle Symposium, Versailles, France, pp. 646-651, Jun. 2002.
[15] Labayrade, R., Royere, C., Gruyer, D., and Aubert, D., “Cooperative Fusion for Multi-obstacles Detection with the Use of Stereovision and Laser Scanner,” Autonomous Robots, Hingham, Vol. 19, No. 2, pp. 117-140, Sep. 2005.
[16] Labayrade, R., Royere, C., Hautiere, N., and Aubert, D., “ Long Range Obstacle Detection Using Laser Scanner and Stereovision,” IEEE Intelligent Vehicles Symposium, Tokyo, Japan, pp. 182-187, Jun. 2006.
[17] Labayrade, R., Royere, C., and Aubert, D., “A Collision Mitigation System Using Laser Scanner and Stereovision Fusion and its Assessment,” IEEE Intelligent Vehicles Symposium, Versailles, France, pp. 441-446, Jun. 2005.
[18] Wijesoma, W. S., Kodagoda, K. R. S., Balasuriya, A. P., and Teoh, E. K., “Road Edge and Lane Boundary Detection Using Laser and Vision,” IEEE International Conference Intelligent Robots and Systems, Maui, USA, Vol. 3, pp. 1440-1445, Oct. 2001.
[19] Gern, A., Franke, U., and Levi, P., “Advanced Lane Recognition-fusing Vision and Radar,” IEEE Intelligent Vehicles Symposium, Dearborn, USA, pp.45-51, Oct. 2000.
[20] Ma, B., Lakshmanan, S., and Hero, A. O., “Pavement Boundary Detection via Circular Shape Models,” IEEE Intelligent Vehicles Symposium, Dearborn, USA, pp.644-649, Oct. 2000.
[21] Yamaguchi , Y., Sengoku, M., and Motooka, S., “Using a Van-mounted FM-CW Radar to Detect Corner-reflector Road-boundary markers,” IEEE Transactions on Instrumentation and Measurement, Vol. 45, pp.793-799, 1996.
[22] Gonzalez, R. C., and Woods, R. E., Digital Image Processing, Second Edition, Prentice-Hall, New Jersey, 2002.
[23] Suzuki, K., Horiba, I., and Sugie, N., “Fast Connected-Component Labeling Based on Sequential Local Operations in the Course of Forward Raster Scan Followed by Backward Raster Scan,” Proceeding of 15th International Conference on Pattern Recognition, Barcelona, Spain, Vol. 2, pp. 434-437, Sep. 2000.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊