跳到主要內容

臺灣博碩士論文加值系統

(18.205.192.201) 您好!臺灣時間:2021/08/05 09:56
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:林裕峰
研究生(外文):Yu-Feng Lin
論文名稱:基於視訊處理之即時車流分析暨計數系統之研究
論文名稱(外文):The Study on Real-Time Vehicle-Flow Analyzing and Counting System Based on Video Processing
指導教授:陳昭和
指導教授(外文):Thou-Ho Chen
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子與資訊工程研究所碩士班
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:2007
畢業學年度:95
語文別:中文
中文關鍵詞:車輛計數車輛分類物件擷取
外文關鍵詞:vehicle countingvehicle classificationobject extraction
相關次數:
  • 被引用被引用:5
  • 點閱點閱:491
  • 評分評分:
  • 下載下載:70
  • 收藏至我的研究室書目清單書目收藏:3
近年來,隨著科技的發展與進步,交通問題日愈嚴重,由於增建之道路有限,交通建設緩不濟急,塞車問題不僅造成運輸機動性低落,亦付出相當的社會成本。因此,大量的相關研究便隨之孕育而生。在這些研究當中,智慧型交通運輸系統(Intelligent Transportation System,ITS)則是被用來解決交通問題的方法之一,它整合了通訊、控制、電子、與資訊等相關技術,使有限的資源作最有效的利用,以提高人民生活品質及經濟競爭力。
本論文利用電腦視覺與視訊處理之技術,發展出一套可應用於交通監測之即時車流分析暨計數系統。透過道路監視攝影機所擷取的車流影像,運用移動物切割演算法,擷取畫面中之移動車體輪廓,再以影像增強的方法提取出完整的車體遮罩。利用此車體的遮罩與包含車體的最小矩形的分析,得到該車體的相關特徵;其後依據車體特徵的相關性分析與量測前後影像間移動物體的距離達到車輛追蹤的目的。並應用統計的方式加上時間域的考量進行車道上車輛的分類以增加車體分類的強健性。最後依照追蹤車體的數據對所分類的移動車體估算出行進檢測區的瞬間車速與車體數量的統計,以達到在交通監視系統上分析車流的功能。
我們會以一連串的影像序列來驗證我們的實驗結果,其中包含車輛追蹤、車體分類、以及車速的擷取。並與人工計數的結果相比較,在正常情況下,車輛計數的正確率可達90%左右,且符合即時之需求。
In recent year, the traffic density is rapidly increasing in most countries, making the traffic problem become one of major issues today. Therefore, a large number of researches are presented. The Intelligent Transportation System (ITS) is one of the best ways to solve the traffic problem. An ITS combines telecommunications, electronics, computer application, control and sensor technologies to improve traffic safety and efficiency.
In this thesis, a vehicle counting method is proposed to measure traffic flow based on video processing. The motion detection and objection segmentation are used to grab the vehicle mask. By analyzing the mask of the vehicles and their minimal boundary boxes, the meaningful and obvious features of vehicles are extracted. Tracking moving targets is achieved by comparing the extracted features and measuring the minimal distance between two temporal images. The temporal classifier with statistical method is presented to increase the accuracy of vehicle classification. Finally, the velocity of the vehicles and the vehicle flow through the field of vision can be extracted from the image sequences by analyzing the data of vehicles. The experiment results show that the accuracy is about 90%, and this method can meet the requirement of real-time.
摘要
ABSTRACT
誌謝
目錄
圖目錄
表目錄
第一章、 緒論
1.1. 研究背景
1.2. 相關研究
1.2.1. 移動目標偵測
1.2.2. 物體追蹤
1.3. 系統架構與流程
1.4. 論文架構
第二章、 移動目標偵測
2.1. 適應性背景相減
2.1.1. 創建背景模型
2.1.2. 移動區域偵測
2.1.3. 背景更新機制
2.2. 影像增強
2.2.1. 形態運算
2.2.2. 雜訊濾除
2.2.3. 連通區域標記
2.3. 多物件擷取
第三章、 特徵擷取與物體追蹤
3.1. 特徵擷取
3.1.1. 特徵與座標的關係
3.1.2. 重心座標
3.1.3. 幾何比例
3.1.4. 緊密度
3.2. 車體追蹤
3.2.1. 移動物體的追蹤法則
3.2.2. 移動物體在影片前後的對應關係
第四章、 物件分類
第五章、 車速估測
5.1. 攝影機視場距離標定演算法則
5.2. 目前所使用的測速法則
第六章、 實驗結果
6.1. 系統說明
第七章、 結論與未來展望
參考文獻
相關著作發表
[1] I. Masaki, 1998, “Machine-version system for intelligent transportation systems”, IEEE Intelligent Systems, pp.24-31
[2] 陳志賢,基於視訊處理的即時人流計數系統之研究,國立高雄應用科技大學電子工程系碩士論文,中華民國九十五年六月
[3] C. R. Wren, A. Azarbayejani, T. Darrell and A. P. Pentland, 1997, ”Pfinder: Real-Time Tracking of Human Body”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, No. 7, July.
[4] C. Stauffer and W. Grimson, 1999, “Adaptive background mixture models for real-time tracking”, in Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, June.
[5] D. S. Lee, J. J. Hull, and B. Erol, 2003, “A Bayesian Framework for Gaussian Mixture Background Modeling”, in Proc. of International Conference on Image Processing.
[6] C. Rider, O. Munkelt and H. Kirchner, 1995, ”Adaptive Background Estimation and Foreground Detection using Kalman-filter”, International Conference on Recent Advances in Mechatronics, pp.193-199.
[7] A. J. Lipton, H, Fujiyoshi and R. Patil, 1998, “Moving target classification and tracking from real-time video”, in Proc. IEEE Workshop on Applications of Computer Vision, pp. 8-14, October.
[8] C. Zhang and M. Y. Siyal, 2000,“A New Segmentation Technique for Classification of Moving Vehicles,” vehicular Technology Conf. Proceeding, 2000 VTC 2000-Spring Tokyo, IEEE 51st, Vol. 1, pp. 323-326, May
[9] R. Collins, A. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto, and O. Hasegawa, 2000, A system for video surveillance and monitoring: VSAM final report, Technical CMU-RI-TR-00-12.
[10] S. Galic and S. Loncaric, 2000,“Spatio-temporal image segmentation using optical flow and clustering algorithm”, Proceedings of the First International Workshop on Image and Signal Processing and Analysis, pp. 63-68, June
[11] D. Gutchess, M. Trajkonic, E. Cohen-Solal, D. Lyons and A. K. Jain, 2001, ”A background model initialization algorithm for video surveillance”, the 8th IEEE Int. Conf. on Computer Vision, pp. 733-740.
[12] D. Beymer, P. McLauchlan, B. Coifman, J. Malik, 1997, “A real-time computer vision system for measuring traffic parameters”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 495-501
[13] H. Sidenbladh and M. Black, 2000, “Stochastic tracking of 3D human figures using 2D image motion,” in Proc. European Conf. Computer Vision, Dublin, Ireland, pp. 702–718
[14] N. Peterfreund, 2000, “Robust tracking of position and velocity with Kalman snakes”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 564-569.
[15] Hironobu Fujiyoshi, Alan J. Lipton, 1998, “Real-time human motion analysis by image skeletonization” 4th IEEE Workshop on Applications of Computer Vision (WACV'98), Princeton, New Jersey, October.
[16] R. Polana, R. Nelson, 1994,“Low level recognition of human motion”, In Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 77-82.
[17] Bing-Fei Wu, Shin-Ping Li, Yuan-Hsin Chen, 2005, ”A real-time multiple-vehicle detection and tracking system with prior occlusion detection and resolution”, IEEE International Symposium on Signal Processing and Information Technology, pp.311 - 316, Dec.
[18] S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, 2000, “Tracking groups of people”, Computer Vision and Image Understanding, vol. 80, pp. 42-56.
[19] Kenneth R. Castleman, Digital Image Processing, Prentice Hall Press,1996.
[20] 許詠泰,應用於監視系統之強健式整合性的即時移動物體偵測與追蹤演算法,元智大學機械工程研究所碩士論文,中華民國九十二年七月
[21] S. D. Joan. C. M. Liu, C. C. Chang, and Z. Chen, 1994, “A New Algorithm and its VLSI Architecture Design for Connected Component Labeling”, IEEE International Symposium on Circuits and Systems, vol.2, pp. 565-568, May
[22] 林育生,視訊中車輛與行人之辨認與追蹤,輔仁大學電子工程系碩士論文,中華民國九十三年一月
[23] Liu Bo, Zhou Heqin, 2003, “Using object classification to improve urban traffic monitoring system”, IEEE International Conference on Neural Networks and Signal Processing, Vol. 2,pp.1155-1159,Dec.
[24] 鄭世杰,應用於交通監視系統上之行人與車輛分類演算法,國立成功大學電腦與通信工程研究所碩士論文,中華民國九十五年六月
[25] Wakabayashi, Y., Aoki, M., 2005, “Traffic flow measurement using stereo slit camera”, IEEE Intelligent Transportation Systems, pp.198 - 203, Sept.
[26] 張馥麟,交通監視系統之物件追蹤、陰影移除與碰撞事件偵測演算法,國立成功大學電腦與通信工程研究所碩士論文,中華民國九十五年六月
[27] 林清波,基於即時影像追蹤之交通監測系統,國立交通大學電機與控制工程學系碩士論文,中華民國九十一年七月
[28] Sadeghi, M., Fathy, M., 2006, “A Low-Cost Occlusion Handling Using a Novel Feature in Congested Traffic Images”, IEEE International Conference on Intelligent Transportation Systems, pp. 522-527
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top