跳到主要內容

臺灣博碩士論文加值系統

(3.231.230.177) 您好!臺灣時間:2021/08/04 05:21
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:林畇鈺
研究生(外文):Yun-Yu Lin
論文名稱:夜間微弱光源環境下之多物體移動追蹤
論文名稱(外文):Multiple objects tracking in a night environment with weak lamplight
指導教授:曾逸鴻曾逸鴻引用關係
指導教授(外文):Yi-Hong Tseng
學位類別:碩士
校院名稱:大葉大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
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
論文頁數:47
中文關鍵詞:智慧型視訊監控系統亮度均衡化背景相減多物體追蹤
外文關鍵詞:intelligent video monitoring systemequalizationbackground subtractionmulti-object tracking
相關次數:
  • 被引用被引用:0
  • 點閱點閱:201
  • 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:2
隨著經濟逐年發展,個人住家安全也日益受到重視,如何應用平價的視訊擷取設備,做到限制空間之人員出入與移動監控,是近年來電腦視覺領域的研發重點。建構一個智慧型視訊監控系統,除了可節省人力成本,並可提供即時的監控與警示。
目前的智慧型視訊監控系統,多應用在日間或明亮的環境,但多數犯罪活動都發生在昏暗的夜晚期間。因此,本研究主要針對室內環境的夜間安全監控,使用平價且普及的網路攝影機作為視訊擷取設備,發展可應用於夜間的視訊監控系統。本系統除了可改善光線不足所造成的前景物體偵測不佳外,並著重在處理夜間環境下的多移動物體追蹤。首先,畫面影像經過改良式亮度均衡化後,可得到較清晰的影像。利用背景相減方法,可擷取出前景物體。抽取每個前景物體影像的顏色與位置等特徵後,利用比對持續記錄的穩定與移動特性,即可對不同前景物體做移動追蹤。實驗結果顯示,本研究所開發的系統可以有效地在昏暗環境下進行多物體追蹤,驗證了本研究所提方法的可行性。
As economy is developed year by year, the personal household security is also concerned day by day, how to apply the inexpensive video equipment to monitor the people enter, exit or move in limited space is the emphasis of the researches on computer vision field in recent years. So, constructing an intelligent video monitoring system not only saves the human cost, but also provides the surveillance and warning real time.
Now the intelligent video monitoring systems always apply to in the daytime or bright environments, but many criminal actions occur in the dim night. Therefore, this paper is mainly aimed at indoor secured surveillance at night, and uses the inexpensive digital web camera for capturing video frames developing to apply to video monitoring systems at night. In addition to improve insufficient light causing object detected the failure, this system stress to handle multi-object detecting and tracking at night.
First of all, frames can be clearer by ameliorated equalization method. Follow,using background subtraction can capture and combine to form fore-object, and judge whether it is the mankind or not. After collecting the features of each fore-object such as colors and position etc.., it can track various moving object by comparing with sustained recording static and dynamical character. Experimental results demonstrate this paper develop the system can effectively track multiple object in the dim environment.
中文摘要 ..................... iii
英文摘要 ..................... iv
誌謝詞 ..................... v
內容目錄 ..................... vi
表目錄  ..................... viii
圖目錄  ..................... ix
第一章  緒論................... 1
  第一節  研究背景動機............. 1
  第二節  研究目的與方法............ 2
  第三節  研究限制............... 3
  第四節  論文架構............... 4
第二章  文獻探討................. 5
  第一節  前景物體偵測............. 5
  第二節  前景物體追蹤............. 7
  第三節  夜間移動物體偵測........... 8
第三章  前景物體偵測............... 10
  第一節  建構背景模型............. 10
  第二節  背景相減............... 16
第三節  前景區域偵測與調整.......... 19
第四章  前景物體追蹤............... 23
  第一節  物體特徵抽取............. 23
  第二節  前景區域比對............. 26
  第三節  前景物體追蹤............. 31
第五章  實驗結果分析............... 37
  第一節  實驗結果............... 37
  第二節  錯誤分析............... 39
第六章  結論................... 43
參考文獻 ..................... 44
Aggarwal, J. K., & Zhou, Q. (2006). Object tracking in an outdoor environment using fusion of features and cameras. Computer Vision and Image Understanding, 24 (11), 1244-1255.

Chem, M. Y., & Hou, P. C. (2003). The Lane Recognition and Vehicle Detection at Night for A Camera-Assisted Car on Highway. Proceedings of the IEEE International Conference on Robotics & Amtomation, 2, 2110- 2115.

Chen, T., Wu, Q. H., Rahmani-Torkaman R., & Hughes, J. (2002). A pseudo top-hat mathematical morphological approach to edge detection in dark regions. Pattern Recognition, 35 (1),199-210.

Cheng, F. H., & Chen, Y. L. (2006). Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recognition, 39 (6), 1126-1139.

Collins, R. T., Liu, Y., & Leordeanu, M. (2005). Online Selection of Discriminative Tracking Features. IEEE Transactions on Pattern Analysis and Machine Intelligentce, 27 (10), 1631-1643.

Fang, Y., Yamada, K., Ninomiya, Y., Horn, B. K. P., & Masaki, I. (2004). A shape-independent method for pedestrian detection with far-infrared images. IEEE Transactions on Vehicular Technology, 53 (5), 1679-1697.

Gonzalez, R. C., & Woods, R. E. (2002) Digital Image Processing(2nd ed.), Prentice Hall.

Kang, H., & Kim, D. (2005). Real-time multiple people tracking using competitive condensation. Pattern Recognition, 38(7), 1045-1058.

Kim, J. B., & Kim, H. J. (2003). Efficient region-based motion segmentation for a video monitoring system. Pattern Recognition Letters, 24 (1-3), 113-128.

Lerdsudwichai, C., Abdel-Mottaleb, M. & Ansari, A. N. (2005). Tracking multiple people with recovery from partial and total occlusion. Pattern Recognition, 38(7), 1059-1070.

Liu, X., & Fujimura, K. (2004). Pedestrian Detection Using Stereo Night Vision. IEEE Transactions on Vehicular Technology, 53 (6), 1657-1664.

Magee, D. R. (2004). Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing, 22 (2), 143-155.

McKenna, S. J., Jabri, S., Duric, Z., Rosenfeld, A., & Wechsler, H. (2000). Tracking Groups of People. Computer Vision and Image Understanding, 80 (1), 42-56.

Pai, C. J., Tyan, H. R., Liang, Y. M., Liao, H. Y. M., & Chen, S. W. (2004). Pedestrian detection and tracking at crossroads. Pattern Recognition Letters, 37 (5), 1025-1034.

Rafael, M. S., Eugenio, A., & Miguel, G.. S. (2007). People detection and tracking using stereo vision and color. Image and Vision Computing, 25 (6), 995-1007.

Rowley, H. A., & Rehg, J. M. (1997). Analyzing articulated motion using expectation-maximization. In Proceedings of the IEEE International Conference on Pattern Recognition, 935–941.

Senior, A., Hampapur, A., Tian, Y. L., Brown, L., Pankanti, S., & Bolle, R. (2006). Appearance models for occlusion handling. Computer Vision and Image Understanding, 24 (11), 1233-1243.

Suzuki, K., Horiba, I., & Sugie, N. (2003). Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1582-1596.

Tissainayagama, P., & Suterb, D. (2005). Object tracking in image
sequences using point features. Pattern Recognition, 38(1), 105-113.

Tseng, Y. H. & Lin, C. H. (2006). Housebreaker detection by analyzing moving light sources in a dark indoor environment, Proceedings of the 19th Conference on Computer Vision, Graphics and Image Processing, 720-727.

Tseng, Y. H. & Xiao, H. Z. (2005). Background model construction and maintenance in a video surveillance system. In Proceedings of the 18th Conference on Computer Vision, Graphics and Image Processing, 303-309

Wang, H., & Suter, D. (2006). A consensus-based method for tracking: Modelling background scenario and foreground appearance. Pattern Recognition, 40 (3), 1091-1105.

Xu, F., Liu, X., & Fujimura, K. (2005). Pedestrian detection and tracking with night vision. IEEE Transactions on Intelligent Transportation System, 6 (1), 63-71.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top