跳到主要內容

臺灣博碩士論文加值系統

(3.235.140.84) 您好!臺灣時間:2022/08/15 02:08
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

: 
twitterline
研究生:白家榮
研究生(外文):Chia-Jung Pai
論文名稱:十字路口行人之偵測及追蹤
論文名稱(外文):Pedestrian detection and tracking at crossing
指導教授:廖弘源廖弘源引用關係陳世旺陳世旺引用關係
指導教授(外文):Hong-Yuan Mark LiaoSei-Wang Chen
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:資訊教育研究所
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:40
中文關鍵詞:影像處理行人模組走路節奏動態圖形比對
外文關鍵詞:image processingpedestrian modelwalking rhythmdynamical graph matching
相關次數:
  • 被引用被引用:7
  • 點閱點閱:510
  • 評分評分:
  • 下載下載:147
  • 收藏至我的研究室書目清單書目收藏:2
  本文利用影像處理的技術,提出一個路上行人偵測的方法,此方法結合了行人模組(pedestrian model)與走路節奏(walking rhythm)的特性,利用空間與時間的資訊,準確地將行人擷取出來,並且利用動態圖形比對(dynamical graph matching)的方法來追蹤行人。將來可以結合通訊的技術,來通知駕駛者路口的資訊,如此駕駛者便可以有更加充裕的時間來反應交通狀況,行人的安全也比較獲得保障。
This paper presents a system for pedestrian detection and tracking by using image processing techniques. It is an important task to protect pedestrians from impact, so we have to detect pedestrians fast and automatically. We propose a method which combines the pedestrian model and the walking rhythm of pedestrians. By using these spatial and temporal information, the detecting result will be accurate. And the technique of dynamical graph matching is used to track pedestrians. It is possible to inform the driver of the situation of the crossing by cooperating the techniques of communication, and the driver will have abundant time to cope with the traffic conditions. The safety of pedestrians can be ensured.
第一章、簡介
第二章、相關研究與提出的方法
第四章、物體的追蹤
第五章、行人的辨識
第六章、實驗結果
第七章、結論
參考文獻
[1] A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi, “Shape-based pedestrian detection,” Proc. of the IEEE Intelligent Vehicle Symposium, pp.215-220, 2000.
[2] A. Prati, I. Mikić, C. Grana, and M. M. Trivedi, “Shadow detection algorithms for traffic flow analysis: a comparative study,” IEEE Conf. on Intelligent Transportation System, pp. 340-345, 2001
[3] C. Curio, J. Edelbrunner, T. Kalinke, C. Tzomakas, and W. V. Seelen, “Walking pedestrian recognition,” IEEE Trans. on Intelligent Transportation System, vol. 1, no. 3, pp. 155-163, 2000.
[4] C. Papageorgiou, and T. Poggio, “Trainable pedestrian detection,” Proc. of ICIP, pp. 25-28, 1999.
[5] C. Wöhler, and J. K. Anlauf, “Real-time object recognition on image sequences with the adaptable time delay neural network algorithm — applications for autonomous vehicles,” Image and Vision Computing, vol. 19, no. 9-10, pp. 593-618, 2001.
[6] D. M. Gavrila, “Sensor-based pedestrian protection,” IEEE Intelligent Systems, vol. 16, pp. 77-81, 2001.
[7] H. Mori, N. M. Charkari, and T. Matsushita, “On-line vehicle and pedestrian detections based on sign pattern,” IEEE Trans. on Industrial Electronics, vol. 41, no. 4, pp. 384-391, 1994.
[8] H. T. Chen, H. H. Lin, and T. L. Liu, “Multi-object tracking using dynamical graph matching,” IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 210-217, 2001.
[9] J. Heikkilä, and O. Silvén, “A real-time system for monitoring of cyclists and pedestrians,” IEEE Proc. on Visual Surveillance, pp. 74-81, 1999.
[10] L. C. Fu, and C. Y. Liu, “Computer vision based object detection and recognition for vehicle driving,” IEEE Proc. on Robotics & Automation, pp. 2634-2641, 2001.
[11] L. Zhao, and C. E. Thorpe, “Stereo- and neural network-based pedestrian detection,” IEEE Trans. on Intelligent Transportation Systems, vol. 1, no. 3, pp. 148-154, 2000.
[12] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, “Pedestrian detection using wavelet templates,” IEEE Proc. on Computer Vision and Pattern Recognition, pp. 193-99, 1997.
[13] O. Masoud, and N. P. Papanikolopoulos, “A novel method for tracking and counting pedestrians in real-time using a single camera,” IEEE Trans. On Vehicular Technology, vol.50, no.5, pp.1267-1278, 2001.
[14] O. Masoud, and N. P. Papanikolopoulos, “Robust pedestrian tracking using a model-based approach,” IEEE Conf. on Intelligent Transportation Systems, pp. 338-343, 1997.
[15] S. Yasutomi, H. Mori, and S. Kotani, “Finding pedestrians by estimating temporal-frequency and spatial-period of the moving objects,” Robotics and Autonomous Systems, vol.17, pp. 25-34, 1996.
[16] U. Franke, D. Gavrila, S. Görzig, F. Lindner, F. Paetzold, and C. Wöhler, “Autonomous driving goes downtown,” IEEE Intelligent Systems, vol. 13, pp. 40-48, 1998.
[17] V. Philomin, R. Duraiswami, and L. Davis, “Pedestrian tracking from a moving vehicle,” Proc. of the IEEE Intelligent Vehicles Symposium, pp. 350-355, 2000.
[18] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, Cambridge University Press, pp. 572-575, 1992.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top