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Title: 教室環境內多重人臉偵測與定位研究
Multiple Human Face Detection and Location in Classroom
Authors: 李忠謀
Greg Lee
Chang, Shu-How
Keywords: 多人臉偵測
multiple face detection
face location
object tracking
Issue Date: 2010
Abstract: 人臉及人物偵測在智慧型監視環境中是一塊相當重要的研究主題,這項技術對於人類的生活具有相當廣泛的影響。以學習來說,將人臉偵測應用於教室環境中,能夠做為觀察學生上課行為模式的參考資料,他們的行為模式可以提供授課老師更多學習上的回饋。更能進一步延伸成上課點名系統,將點名工作自動化處理,減少老師在上課中花費的時間,提升教學品質。 本研究針對教室環境實作多人臉的偵測,主要分成兩部份,利用人臉與人物的特性,以階層性的AdaBoost方法搭配過濾取得人臉。首先以人臉為主,實作一個改良型分類器,取出影像中所有可能的人臉區域。另外,加入人物偵測的方法增加人臉可靠度,以提升整體研究的正確率。最後我們提出一套類似物件追蹤方法的機制,Bubble-Developing Mechanism,讓人臉影像具有時間與定物特性,還能大幅提升偵測率,在單人偵測與多人偵測的實驗影片最高可達93%和89%的偵測率。
Face detection and human detection are important in all surveillance method applications. In classroom, we can use detection to assist us to observe student activities. Their response will give some suggestions to teacher, and teacher can improve the teaching. Furthermore, it can extend automatically real-time roll call system to help teacher. We propose a new detection method in classroom. Our method employ a combination of AdaBoost classify faces, applied filter and HOG find trustworthy human face. Bubble-Developing Mechanism (BDM) is a similar object tracking method. It’s an easy way to solve the continuous problem in video sequence or live video. Bubble means individual face results in each of frame and they will have weights just like age. Growth over time, bubbles grow old or die. Because BDM have characteristics of time and continuous, it can enhance the performance of our method. In experiment results, improve AdaBoost and applied filters have a better frame rate than original AdaBoost for real-time face detection. BDM can achieve detection rate from 72% to 94% in single person detection and have average 85% detection rate in multiple people environment.
Other Identifiers: GN0697470054
Appears in Collections:學位論文

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