Method for detection of a student's pose in a multi-scene classroom based on meta-learning

被引：0
|
作者：
Qian Z. [1 ,2 ]
Gao C. [1 ,2 ]
Ye S. [1 ,2 ]
机构：
[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing
[2] Chongqing Key Laboratory of Signal and Information Processing, Chongqing
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2021年 / 48卷 / 05期
关键词：
Adaptive domain optimizer; Domain adaptation; Few shot learning; Meta-model; Pose detection;
D O I：
10.19665/j.issn1001-2400.2021.05.008
中图分类号：
学科分类号：
摘要：
To solve the problem of domain shift in different classroom scenes, this paper proposes a multi-scene classroom pose detection method based on meta-learning. In this method, a pose detection meta-model and a domain adaptive optimizer with learnable parameters are designed. Besides, the offline learning mode and online learning mode are combined to realize the fast domain adaptation of the detection model in a specific classroom scene. In the offline learning stage, the method trains the parameters of the pose detection meta-model and the adaptive domain optimizer through two-layer training. In the online learning stage, guided by the adaptive domain optimizer, the meta-model can quickly adapt to the data distribution of the scene with a few labeled images. In addition, this paper also proposes an external training optimizer which can make the double-layer training more stable. Experiments show that the detection accuracy of this method in multi-scene classroom pose detection dataset is better than that of the current popular object detection models, and that it also has a good domain adaptation effect for new scenes with a few labeled images. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：58 / 67
页数：9
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