Hand-Raising Gesture Detection in Real Classroom

Abstract

This paper proposes a novel method for hand-raising detection in the real classroom environment. Different from traditional motion detection, the hand-raising detection is quite challenging in the real classroom due to complex scenarios, various gestures, and low resolutions. To solve these challenges, we first build up a large-scale hand-raising data set from thirty primary schools and middle schools of Shanghai, China. Then we propose an improved R-FCN to solve the above-mentioned challenges. Specifically, we first design an automatic detection templates algorithm for various gestures of hand-raising detection. Second, for better detection of the small-size hands, we present a feature pyramid to simultaneously capture the detail and highly semantic features. Incorporating these two strategies into a basic R-FCN architecture, our model achieves impressive results on real classroom scenarios. After a wide test, the accuracy of the hand-raising detection achieves 85% on average, which can satisfy the real application.