Fast and Accurate Hand-Raising Gesture Detection in Classroom

This paper proposes a fast and accurate method for hand-raising gesture detection in classrooms. Our method is based on a one-stage detector, CenterNet, which significantly reduces the inference time. Meanwhile, we design three mechanisms to improve the performance. Firstly, we propose a novel suppression loss to prevent easy and hard examples from overwhelming the training process. Secondly, we adopt a deep layer aggregation network to fuse semantic and spatial representation, which is effective for detecting tiny gestures. Thirdly, due to less variation in aspect ratios, we only regress single width property to predict whole bounding box. Thus achieving a more accurate result. Experiments show that our method achieves 91.4% mAP on our hand-raising dataset and runs at 26 FPS, 6.7x faster than the two-stage ones.