A Dynamic Head Gesture Recognition Method Based on 3D Convolutional Two Stream Network Fusion

被引：0
|
作者：
Xie J.-L. [1 ]
Zhang B.-T. [1 ]
Lü Q. [1 ]
机构：
[1] School of Automation, Hangzhou Dianzi University, Hangzhou
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2021年 / 49卷 / 07期
关键词：
Action recognition; Computer vision; Deep learning; Dynamic head gesture; Human computer interaction; Two stream network;
D O I：
10.12263/DZXB.20201183
中图分类号：
学科分类号：
摘要：
Present vision based on dynamic head gesture recognition algorithms usually have disadvantages in generalization and recognition rate, and head mounted sensors are expensive and inconvenient. In view of the above problems, a dynamic head gesture recognition algorithm without head mounted sensors is proposed. Using this method based on two stream 3DCNN(3D Convolutional Neural Network), the dense optical flow is generated by head movements, the original data and dense optical flow are put into the motion feature extractor in parallel, and finally, features are fused. Experimental results show that the proposed algorithm has higher recognition accuracy and better generalization than the artificial feature extraction and C3D(Convolutional 3D) methods, and its recognition rate is as good as those head mounted sensors. © 2021, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：1363 / 1369
页数：6
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