Abstract
In view of the problems of the high time cost and low accuracy of manual supervision in traditional classroom teaching, this paper proposes a human body pose recognition system based on teaching interaction. The enhanced basic network (ResNext-101 + FPN) was used in Mask R-CNN to extract the features of the input images. Then based on the behavior analysis algorithm and face detection data, the behavior data of each student in the classroom were obtained. Moreover, the behavior data were applied to support multi-dimensional visualization. The experimental results show that the system can timely and effectively reflect the learning status of students, and help teachers accurately grasp the classroom learning state of students, so as to adjust teaching strategies in a targeted way and help improve the quality of teaching.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jun, Z., Bingjiang, G.: Research on classroom behavior detection based on SSD algorithm. Comput. Knowl. Technol. 16(34), 212–214 (2020)
Xiuling, H., Fan, Y., Zengzhao, C., Jing, F., Yangyang, L.: Modern Educational Technology 30(11), 105–112 (2020)
Yingna, D., Hong, Z., Wei, L., Huifang, Q.: A crowd target segmentation method based on attitude model. Comput. Eng. 36(07), 195–197 (2010)
Zhang, H.Y.: Design and Implementation of Classroom Learning Behavior Measurement System, pp. 6–39. Huazhong University of Science and Technology, Wuhan (2016)
Zhe, C., Simon, T., Wei, S.E., et al.: Realtime multi-person 2D pose estimation using part affinity fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Yaole, W., Linyan, L., Xinru, S., Fuyuan, H.: An improved mask-RCNN feature fusion instance segmentation method. Comput. Appl. Softw. 36(10), 130–133 (2019)
Wang, S., Zhao, L., He, W., Li, F.: Detection of train driver’s eye state based on improved ASM algorithm. Transsens. Microsyst. 38(05), 129–132 (2019)
Zhong, W., Liu, X., Yang, K., Li, F.: Multi-objective blade segmentation and recognition based on mask-RCNN in complex background. Acta Agriculturae Zhejiangensis 32(11), 2059–2066 (2020)
Lin, T.Y., Dollar, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society (2017)
Fan, Z., Hongyuan, W., Ji, Z.: An improved pedestrian fine-grained detection algorithm based on mask R-CNN. Comput. Appl. 39(11), 3210–3215 (2019)
Fenghui, L.: Image noise processing based on mathematical morphology. Inf. Technol. 30(6), 45–46,142 (2006)
Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 1867–1874 (2014)
Acknowledgement
This parper is supported by the 2019 Innovation and Entrepreneurship Training Program for College Students in Jiangsu Province (Project name: Human posture recognition based on teaching interaction, No. 201911460042Y).
This parper is supported by the National Natural Science Foundation of China Youth Science Foundation project (Project name: Research on Deep Discriminant Spares Representation Learning Method for Feature Extraction, No. 61806098).
This parper is supported by Scientific Research Project of Nanjing Xiaozhuang University (Project name: Multi-robot collaborative system, No. 2017NXY16).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, K., Wang, Y., Li, Y. (2021). Human Body Pose Recognition System Based on Teaching Interaction. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-16-5940-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5939-3
Online ISBN: 978-981-16-5940-9
eBook Packages: Computer ScienceComputer Science (R0)