In this paper, we propose an automatic hand-raiser recognition algorithm to show who raise their hands in real classroom scenarios, which is of great importance for further analyzing the learning states of individuals. To recognize the hand-raisers, we divide the hand-raiser recognition into three subproblems, including hand-raising detection, pose estimation, and matching the raised hands to students. Several challenges exist while dealing with the above-mentioned subproblems, such as low

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... ution of the back row for keypoints detection, the motion distortion caused by hand raising in pose estimation, and various complex situations for matching. To solve these challenges, we first adopt an improved R-FCN algorithm for hand-raising detection, whose effectiveness has been demonstrated. Secondly, we present a novel PAF-based pose estimation algorithm for detecting keypoints of human bodies. The proposed PAF adds scale search and modified weight metric to adapt to the real and complex scenarios. Specifically, scale search improves the detection effect at low resolution by pooling human characteristics in different sizes of pictures, and modified weight metric reasonably utilizes the directional vectors of possible limb connections to optimize the case of motion distortion. Thirdly, a heuristic matching strategy based on the location of hand-raising and keypoints information is proposed to recognize the hand-raisers. Experimental results on six teaching videos in real classrooms have demonstrated the efficiency of the proposed algorithm, and 83% recognition accuracy indicates the potential applications in real classrooms.