Earticle

초록

영어

In this paper, we propose a binarized multi-scale module to accelerate the speed of the pose estimating deep neural network. Recently, deep learning is also used for fine-tuned tasks such as pose estimation. One of the best performing pose estimation methods is based on the usage of two neural networks where one computes the heat maps of the body parts and the other computes the part affinity fields between the body parts. However, the convolution filtering with a large kernel filter takes much time in this model. To accelerate the speed in this model, we propose to change the large kernel filters with binarized multi-scale modules. The large receptive field is captured by the multi-scale structure which also prevents the dropdown of the accuracy in the binarized module. The computation cost and number of parameters becomes small which results in increased speed performance.

목차

Abstract
 1. Introduction
 2. Part Affinity Field based Pose Estimation
 3. Proposed Structure
 4. Experimental Results
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Yong-Gyun Choi [ Department of Computer Engineering, Dongseo University, Busan, 47011, Korea ]
• Sukho Lee [ Department of Computer Engineering, Dongseo University, Busan, 47011, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380