| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程研究所 === 100 === In recently classroom environment, there are more and more teachers using electronic devices to help them easy to understand what students think and how students act. In all gestures, raising hand is the most popular way that students interacting with teachers. In this research, we provided a raising hand recognition system to help teacher to handle all students’ behavior. We use camera in complex background and monitor multiple people. To propose a system that can satisfy all environments and will not re-train after changing environments, we separate the system into two parts: people segmentation and gesture recognition.
In people segmentation part, we use k-means clustering to extract skin color and then use motion to remove skin-liked background. In gesture recognition part, we use histogram of oriented gradient to get the gesture feature and then use SVM to classify. Finally in experimental part, we test 3 scenes to verify our method. When we use the same case to train and test, the correct rate is average 91%. Even we use different day for training/testing, the correct rate can also reach 80%.
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