标题:Head posture detection with embedded attention model
作者:Han J.; Liu Y.
作者机构:[Han, J] Shandong University of Science and Technology Computer Science and Engineering, Qingdao, Shandong, China;[ Liu, Y] Shandong University of Sci 更多
通讯作者:Liu, Y(1017620952@qq.com)
通讯作者地址:[Liu, Y] Shandong University of Science and Technology Computer Science and EngineeringChina;
来源:IOP Conference Series: Materials Science and Engineering
出版年:2020
卷:782
期:3
DOI:10.1088/1757-899X/782/3/032003
摘要:Based on Convolutional Neural Network, the paper presents a compact detection algorithm that can estimate the head pose from a single picture. Our method is based on soft stage wise regression. In order to reduce model complexity, three-dimensional detection of the "pitch, yaw, and roll" of the head posture adopts multi-level classification. Each level of classification requires only a small number of classification tasks and fewer neurons. In order to enhance the feature expression of the algorithm, the attention model is embedded. Attention model includes channel attention structure and spatial attention structure, enhancing the feature expression of the feature map in the two dimensions of the intermediate feature map channel and space. The attention model can be seamlessly integrated into the CNN architecture with low overhead. The experiment proves that the improved algorithm compares the method model proposed by Yang with a smaller complexity of 4.36M and an average absolute error of 0.7%∼0.9%. © Published under licence by IOP Publishing Ltd.
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083707791&doi=10.1088%2f1757-899X%2f782%2f3%2f032003&partnerID=40&md5=042da7cbd5472862cfb1156a53a3d7ef
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