Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 220100057-5.doi: 10.11896/jsjkx.220100057

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Study on Human Pose Estimation Based on Multiscale Dual Attention

MA Wan-yi, ZHANG De-ping   

  1. School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:MA Wan-yi,born in 1996,postgra-duate,is a member of China Computer Federation.Her main research interests include image processing and artificial intelligence modeling.
    ZHANG De-ping,born in 1973,Ph.D,postgraduate supervisor,is a member of China Computer Federation.His main research interests include image processing and artificial intelligence mode-ling.
  • Supported by:
    National Defense Basic Scientific Research Key Program(JCKY2020605C003).

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Abstract: In view of the problem of low discrimination between human body and background in human posture estimation,and incomplete utilization of important feature information in human posture estimation based on HRNet,a human posture estimation method MDA-HRNet based on multiscale dual attention is proposed by using channel and spatial attention mechanism.Conside-ring both of the channel domain and spatial domain,the Ca-Neck and Ca-Block modules combined with channel attention and Sa-Block module combined with spatial attention are designed respectively.Then integrating these modules into the high-resolution network structure,so that the network can pay more attention to the human body area in the image.Moreover,in the Sa-Block module,3×3 and 7×7 convolution kernels are adopted to derive two spatial attention maps of different scales,which makes the ability of the network to comprehensively distinguish human features and background features more remarkable,so as to accurately locate the human body and its key points.The proposed method is tested and verified on MPII data set,and the results show that MDA-HRNet can improve the accuracy of joint point location of human posture estimation effectively.

Key words: Human pose estimation, Channel attention, Spatial attention, Multiscale attention mapping, High resolution network

CLC Number: 

  • TP391.41
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