| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程研究所 === 98 === 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.
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