博碩士論文 93522043 詳細資訊




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姓名 陳怡君(Yi-Chun Chen)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 利用軌跡特徵分析行人異常行為
(Abnormal Pedestrian Behavior Analysis Using Trajectory Features)
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摘要(中) 隨著科技的發展,取像設備價格的降低,監控系統目前已經廣泛的應用在日常生活當中。然而目前使用的監控系統大多只具有錄影的功能,僅能提供事後的資訊,因此便有人提出了智慧型監控系統的概念。利用電腦視覺的方法,在不需要人為的操作之下,讓監控系統能夠自動對攝影機所擷取的影像進行分析,以具有偵測、追蹤、辨識與分析的功能。
本論文提出一個以人類為目標的視訊監控系統,利用行人移動的軌跡特徵,判斷是否發生異常行為。首先利用背景相減法來偵測是否有目標物的存在,並利用目標物的位置、大小與色彩等資訊來追蹤物體。接著我們引入高斯混合模型來表示目標物行為的狀態,利用此模式我們可以有效地辨識與分析目標物的各種行為。最後利用軌跡比對的方式,完成相關事件的軌跡檢索,以供後續研究查詢。
在實驗部份,由於真實行為的影像取得不易,因此我們模擬了數種異常行為。實驗結果顯示,本論文提出的方法可以準確且有效地偵測行人的各種異常行為。
摘要(英) Due to the fast development of computer and video technologies and the cost-down of capturing devices, surveillance systems are gradually widely applied in our daily life. However, the main function of current surveillance systems only focuses on the recording of video event. The developing of automatic and intelligent surveillance systems can detect, track, recognize and analyze moving objects including the behaviors of objects and the occurring of abnormal events, and then issue warring message automatically.
In this thesis, a video surveillance system for abnormal pedestrian behavior analysis is presented. Firstly, background subtraction technique is employed to detect moving objects from video sequences. Then, three key features, including object position, object size, and object color, are extracted to track each detected object. After that, Gaussian Mixture Models (GMM) is introduced to model pedestrians’ behaviors. According to the parameters of the models, different behaviors like walking, running and falling can be successfully recognized and analyzed. Finally, two curve-matching algorithms are employed to complete the trajectory retrieval.
Experimental results show that the proposed method offers great improvements in terms of accuracy, robustness, and stability in the analysis of object behaviors.
關鍵字(中) ★ 行為分析
★ 高斯混合模型
★ 主成分分析
★ 軌跡比對
★ 視訊監控系統
關鍵字(英) ★ Behavior analysis
★ Gaussian Mixture Model
★ Principal Component Analysis
★ Trajectory matching
★ Visual surveillance system
論文目次 Abstract i
摘要 ii
誌謝 iii
目錄 iv
附圖目錄 vi
表格目錄 vii
第一章 緒論 1
1.1 研究動機 1
1.2 相關研究 2
1.3 系統流程 4
1.4 論文架構 7
第二章 前景目標物偵測與追蹤 8
2.1 目標物偵測 10
2.2 陰影偵測與去除 12
2.3 目標物追蹤 15
第三章 異常行為分析 22
3.1 特徵擷取 23
3.2 高斯混合模型(Gaussian Mixture Model, GMM) 25
3.2.1 單一高斯機率密度函數 25
3.2.2 高斯混合模型敘述 26
3.2.3 高斯混合模型的參數估測法 28
3.3 行為辨識 30
第四章 軌跡檢索 31
4.1 取樣 32
4.2 主成分分析(Principal Component Analysis) 33
4.3 軌跡比對 37
4.3.1 以最小投影距離為基礎之衡量矩陣 37
4.3.2 以差異影像為基礎之衡量矩陣 38
第五章 實驗結果 39
5.1 實驗環境與測試資料 39
5.2 目標物偵測 40
5.3 行為分析與分類 44
5.4 相似軌跡檢索 51
第六章 結論與未來研究方向 52
6.1 結論 52
6.2 未來研究方向 53
參考文獻 54
參考文獻 [1] T. Horprasert, D. Harwood and L.S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection”, in Proc. IEEE Frame-Rate Workshop, 1999.
[2] R. Cucchiara, C. Grana, M. Piccardi and A. Prati, “Detecting moving objects, ghosts and shadows in video streams”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, No. 10, pp. 1337-1342, Oct. 2003.
[3] S. Khan and M. Shah, “Tracking people in presence of occlusion”, in Proc. Asian Conf. Computer Vision, Taipei, Taiwan, 2000.
[4] A. Cavallaro, O. Steiger and T. Ebrahimi, “Tracking video objects in cluttered background,” IEEE Trans. Circuits and Systems for Video Technology, Vol. 15, No. 4, Apr. 2005.
[5] F. Brémond and M. Thonnat, “Tracking multiple nonrigid objects in video sequences,” IEEE Trans. Circuits and Systems for Video Technology, Vol. 8, No. 5, pp. 575-584, Sep. 1998.
[6] I. Haritaoglu, D. Harwood and L. S. Davis, “W4: Real-Time Surveillance of People and Their Activities”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 809-830, Aug. 2000.
[7] H. Sidenbladh and M. J. Black, “Learning the statistics of people in images and video”, International Journal of Computer Vision. Vol. 54, Issue 1-3, pp. 183-209, Aug.-Oct. 2003.
[8] P. Dempster, N. M. Laird and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm”, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1, pp. 1-38, 1977.
[9] L.M. Fuentes and S.A. Velastin, “From tracking to advanced surveillance”, in Proc. IEEE Conf. Image Processing, Vol. 2, No. 3, pp. 121-124, Sep. 2003.
[10] I.N. Junejo, O. Javed and M. Shah, “Multi feature path modeling for video surveillance”, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Vol. 2, No. 2, pp. 716-719, Aug. 2004.
[11] L. I. Smith, “A tutorial on Principal Component Analysis”, Feb 2002
[12] Z. Li, A.K. Katsaggelos and B. Gandhi, “Fast video retrieval based on trace geometry matching”, IEE Proc. Vision, Image and Signal Processing, Vol. 152, No. 3, pp. 367-373, June 2005.
[13] C. Tomasi, “Estimating Gaussian Mixture Densities with EM – A Tutorial”, Duke University
[14] R. Bodor, B. Jackson and N. Papanikolopoulos, “Vision-Based Human Tracking and Activity Recognition”, AIRVL, Dept. of Computer Science and Engineering, University of Minnesota.
[15] A. Mecocci, M. Pannozzo, “A completely autonomous system that learns anomalous movements in advanced videosurveillance applications”, in Proc. IEEE Conf. Image Processing, Vol. 2, No. CD-ROM, pp. 586-589, Sept. 2005.
[16] W. Niu, J. Long, D. Han, Y. F. Wang, “Human Acvitity detection and recognition for video surveillance”, IEEE Conf. Multimedia and Expo, Vol. 1, No. 3, pp. 719-722, June 2004.
[17] R. C. Gonzalez, R.E. Woods, 繆紹綱編譯, “Digital Image Processing 2/e,” 台灣培生教育出版股份有限公司出版,普林斯頓國際有限公司發行。
指導教授 范國清(Kuo-Chin Fan) 審核日期 2006-7-6
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