采用元学习的多场景教室学生姿态检测方法

doi:10.19665/j.issn1001-2400.2021.05.008

Abstract

Abstract:

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.

Key words: pose detection, domain adaptation, few shot learning, meta-model, adaptive domain optimizer

CLC Number:

TP391

QIAN Zhihua,GAO Chenqiang,YE Sheng. Method for detection of a student’s pose in a multi-scene classroom based on meta-learning[J].Journal of Xidian University, 2021, 48(5): 58-67.

Figures/Tables 10

References 21

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模型	特征提取网络	坐着	站着	趴着	mAP
CCE-FRCNN^[1]	VGG16	79.27	81.32	66.03	75.54
EFGRNet^[19]	VGG16	87.41	86.52	79.78	84.57
YOLOv4^[20]	CSP-Darknet	87.25	88.80	77.78	84.61
RetinaNet^[21]	ResNet-50-FPN	93.73	91.52	76.38	87.21
Mask R-CNN^[8]	ResNet-50-FPN	93.28	91.40	75.91	86.86
TUOD^[6]	ResNet-50-FPN	93.57	92.10	76.42	87.36
文中方法	ResNet-50-FPN	95.24	96.67	80.82	90.91

方法	域适应	模型	坐着	站着	趴着	mAP
Mask R-CNN	×	ψ	93.28	91.40	75.91	86.86
	√	ψ'	93.26	91.93	76.20	87.13
文中方法	×	元模型	92.10	92.60	67.42	84.04
	√	域适应模型	95.24	96.67	80.82	90.91

方法	坐着	站着	趴着	mAP
Meta-SGD	93.71	94.54	75.83	88.02
MAML	92.11	92.27	74.96	86.45
MAML+ETO	92.53	95.35	75.49	87.79
MAML+DAO	96.55	95.31	77.09	89.64
笔者方法	95.24	96.67	80.82	90.91

方法	坐着	站着	趴着	mAP
Mask R-CNN	53.98	28.45	19.94	34.12
DA^[2]	57.61	30.77	22.88	37.09
SW-DA^[3]	58.89	31.43	24.11	38.14
SC-DA^[4]	59.14	30.96	23.32	37.81
SW-DA-ICR-CCR^[5]	59.86	31.94	24.73	38.84
FAFRCNN^[7]	68.23	49.44	42.29	53.32
文中方法	72.32	55.43	48.16	58.64

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

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