Hand-raising Gesture Detection in Real Classrooms Using Improved R-FCN

This paper proposes a novel method for hand-raising detection in real classroom environments. Different from traditional motion detection, the hand-raising detection is quite challenging due to complex backgrounds, various gestures and low resolutions. To solve these challenges, we build up a large-scale dataset from videos of real classrooms, and propose a novel neural network architecture based on region-based, fully convolutional networks (R-FCN). Specifically, we first design an adaptive templates selection algorithm for various gestures of hand-raising detection. Secondly, for better detection of small-size hands, we design a feature pyramid to simultaneously capture the detail and highly semantic features. Compared with state-of-the-art object detection algorithms, our method achieves impressive results on our hand-raising dataset. After extensive testing, the accuracy of the hand-raising detection achieves 90% in mean Average Precision (mAP), which can satisfy real applications.