文献类型：期刊文献

英文题名：Automatic Aesthetics Evaluation of Robotic Dance Poses Based on Hierarchical Processing Network

作者：Peng, Hua[1,2,3];Ren, Hui[1];Wang, Ziyang[1];Hu, Huosheng[3];Li, Jing[4];Feng, Sheng[1];Zhao, Liping[1];Hu, Keli[1]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Peoples R China;[3]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, England;[4]Shaoxing Univ, Acad Arts, Shaoxing 312000, Peoples R China

年份：2022

卷号：2022

外文期刊名：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

收录：SCI-EXPANDED(收录号：WOS:000864449200009)、、EI(收录号：20224012826422)、Scopus(收录号：2-s2.0-85138651253)、WOS

基金：AcknowledgmentsThis work was supported by the National Natural Science Foundation of China (Grant nos. 61662025, 61871289, and 62271321), and Zhejiang Provincial Natural Science Foundation of China (Grant nos. LY19F020015, LY20F030006, LY20F020011, LTY22F020003, and TY22F025548), and Humanities and Social Sciences Research Foundation of the Ministry of Education of China (Grant nos. 21YJAZH065 and 21YJCZH039).

语种：英文

外文关键词：Intelligent robots - Machine design

外文摘要：Vision plays an important role in the aesthetic cognition of human beings. When creating dance choreography, human dancers, who always observe their own dance poses in a mirror, understand the aesthetics of those poses and aim to improve their dancing performance. In order to develop artificial intelligence, a robot should establish a similar mechanism to imitate the above human dance behaviour. Inspired by this, this paper designs a way for a robot to visually perceive its own dance poses and constructs a novel dataset of dance poses based on real NAO robots. On this basis, this paper proposes a hierarchical processing network-based approach to automatic aesthetics evaluation of robotic dance poses. The hierarchical processing network first extracts the primary visual features by using three parallel CNNs, then uses a synthesis CNN to achieve high-level association and comprehensive processing on the basis of multi-modal feature fusion, and finally makes an automatic aesthetics decision. Notably, the design of this hierarchical processing network is inspired by the research findings in neuroaesthetics. Experimental results show that our approach can achieve a high correct ratio of aesthetic evaluation at 82.3%, which is superior to the existing methods.