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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/35047

    Title: 以色彩分析為基礎之臉部遮蔽偵測應用於ATM監視系統
    Other Titles: Facial occlusion detection based on color analysis for ATM surveillance system
    Authors: 廖維中;Liao, Wei-chung
    Contributors: 淡江大學資訊工程學系碩士班
    洪文斌;Horng, Wen-bing
    Keywords: 臉部遮蔽;動態偵測;橢圓偵測;樣板比對;HSV色彩模型;膚色;Face Occlusion;Motion Detection;Ellipse Detection;Template Matching;HSV Color Model;Skin Color
    Date: 2005
    Issue Date: 2010-01-11 05:56:57 (UTC+8)
    Abstract:   在這篇論文中,我們利用影像處理與電腦視覺技術,對自動櫃員機(ATM)的使用者做臉部遮蔽偵測,並依據不同的遮蔽狀況,提示使用者脫下安全帽或者是口罩。
      本系統在AMD 1800 XP的CPU與512 MB的RAM,輸入影像為320 x 240 RGB全彩(24 bit)Bitmap環境下,平均一秒鐘13畫格,約略在1秒鐘以內可以決定提示一位使用者的訊息;系統正確率則大概在86%以上。
    In this paper, we propose a facial occlusion detection method for Automatic Teller Machine (ATM) users by using image processing and computer vision techniques. According to different occlusion types, the system will issue an appropriate warning message to notify the user to take off his/her safety helmet or mask.
    A digital camcorder capturing user images is used to simulate an ATM surveillance system. The proposed facial occlusion detection method is composed of four steps. First, Sobel edge detection and frames difference are used to find out the pixels of foreground object. Because of human’s head likes an ellipse with longer vertical axis and shorter horizontal axis, we set the position to be a candidate of vertex of minor axis if the vertical projection of vertical edge is high enough in order to speed-up the template matching for ellipse detection. After located the head location, we slice skin color and then do horizontal projection to judge if forehead or mouth are occluded according to the capacity of skin color projection. To prevent erroneous judgment between hair and safety helmet, we find the horizontal edge, and then do top down scan to record if horizontal edge occurred by a accumulated horizontal scanline, thus we can get the lower bound of detection region by the brim of safety helmet. After eliminated helmet occlusion, we judge if mouth is occluded at bottom half of ellipse by checking if there are lower capacity of skin color continuous. Last, system notify user by the majority vote of judgments in image sequence.
    This system works under AMD 1800 XP CPU and 512 MB RAM; the format of input image is 320 x 240 true color (24 bits) Bitmap. The performance of system can reach 13 frames per second (about 0.025 ~ 0.100 seconds to process a frame according to different environment), and the average accuracy of system is higher than 86%.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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