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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9244

    Title: 利用軌跡特徵分析行人異常行為;Abnormal Pedestrian Behavior Analysis Using Trajectory Features
    Authors: 陳怡君;Yi-Chun Chen
    Contributors: 資訊工程研究所
    Keywords: 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;Behavior analysis;Gaussian Mixture Model;Principal Component Analysis;Trajectory matching;Visual surveillance system
    Date: 2006-06-26
    Issue Date: 2009-09-22 11:43:52 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 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。 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.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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