Who Are Raising Their Hands? Hand-Raiser Seeking Based on Object Detection and Pose Estimation

Huayi Zhou, Fei Jiang, Ruimin Shen
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:470-485, 2018.

Abstract

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 resolution 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v95-zhou18a, title = {Who Are Raising Their Hands? Hand-Raiser Seeking Based on Object Detection and Pose Estimation}, author = {Zhou, Huayi and Jiang, Fei and Shen, Ruimin}, booktitle = {Proceedings of The 10th Asian Conference on Machine Learning}, pages = {470--485}, year = {2018}, editor = {Zhu, Jun and Takeuchi, Ichiro}, volume = {95}, series = {Proceedings of Machine Learning Research}, month = {14--16 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v95/zhou18a/zhou18a.pdf}, url = {https://proceedings.mlr.press/v95/zhou18a.html}, abstract = {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 resolution 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.} }
Endnote
%0 Conference Paper %T Who Are Raising Their Hands? Hand-Raiser Seeking Based on Object Detection and Pose Estimation %A Huayi Zhou %A Fei Jiang %A Ruimin Shen %B Proceedings of The 10th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jun Zhu %E Ichiro Takeuchi %F pmlr-v95-zhou18a %I PMLR %P 470--485 %U https://proceedings.mlr.press/v95/zhou18a.html %V 95 %X 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 resolution 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.
APA
Zhou, H., Jiang, F. & Shen, R.. (2018). Who Are Raising Their Hands? Hand-Raiser Seeking Based on Object Detection and Pose Estimation. Proceedings of The 10th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 95:470-485 Available from https://proceedings.mlr.press/v95/zhou18a.html.

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