Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8474
標題: 應用於嬰幼兒監護系統之臉部追蹤與異物偵測演算法設計與實作
Design and Implement of Facial Tracking and Foreign Object Detecting algorithm for Baby Watch and Care System
作者: 呂冠賢
Lu, Hsien-Kuan
關鍵字: 人臉偵測
facial detection
出版社: 電機工程學系所
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摘要: 本論文提出一套具有嬰兒人臉追蹤與異物偵測的數位智慧型監護系統,此系統在嘴巴與鼻子偵測到外來物件時,能即時發出警告,並告知監護者,可取代現階段全靠人力監護的方式,降低監護者的負擔。 本系統可分成三個子系統:人臉定位偵測,人臉追蹤以及異物偵測。在人臉偵測方面:利用膚色範圍找出可能的人臉區域,以達到人臉初步定位的目的。初步定位後,再利用橢圓遮罩定位出人臉輪廓。在人臉追蹤方面:由於第一張影像為了求其準確性處理時間較多,所以會把一些重要的座標資訊儲存起來,接下來會利用這些既有資訊再小範圍區域的搜尋,每收尋一次就更新一次座標資訊,以達到即時的效果。在異物偵測方面:在人臉候選區塊做 Sobel 運算子得到邊緣資訊,並利用五官之間的特性以及嘴唇顏色有別於皮膚顏色的特性,同時擷取五官,而非逐一偵測,得以提高正確性。 實驗結果顯示,在人臉定位偵測部分,使用我們所建立的嬰兒圖像資料庫,正確率可以達到 93%; 在五官偵測部分,正確率可以達到 85%; 使用 Pentium 4的電腦執行程式碼,第一張輸入影像處理需要 0.612杪,接下來每1/6秒偵測一次,執行所需時間約為 0.2秒; 每隔兩秒加入異物偵測功能,加入此一步驟執行時間會增加 0.006杪。
In this study, we will discuss a digital wisdom Baby Watch and Care system that could trace baby's face and detection object. The system will alert user when detecting something around mouth and nose. The way can replace the manpower security in nowadays and reduce user's burden. There are three subsystems, human face position detecting, human face tracing and object detecting. Human face detecting uses skin color to search out the face area and gain initial human face position. After the initial position, using elliptical mask to position the contour of human face. Human face tracing, it takes more time to work first image for accuracy, so some important coordinates information will be stored. Then, using the information to do small range searching, every searching will renew coordinates information to achieve immediate. Object detecting, getting edge information by Sobel operation on chosen human face area and using facial features to compare lips color with skin color, choosing facial features at the same time instead of detecting step by step, by doing this to promote accuracy. The experiment shows, in human face position detecting part, the baby images data we built could achieve 93% accuracy. In facial features part, the accuracy is 85%. First input image processing needs 0.612 second by using Pentium 4 computer program code, then the detecting happens every one sixth second, working time needs 0.2 second. Object detecting will be started every two seconds, it needs more 0.006 second if object detecting works.
URI: http://hdl.handle.net/11455/8474
其他識別: U0005-1208200911172000
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1208200911172000
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