Foot Measurement Based on Deep Alignment Network

Authors

Shi Min, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206,China;;
Hanqin Yao, 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206,China;;
Chunpeng Li, 2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;;
Liangchen Chen, 3. School of Applied Technology, China University of Labor Relations, Beijing 100048, China;

Abstract

Abstract: Foot measurement plays an important role in many areas. Limited by equipment and algorithms, three-dimensional foot measurement cannot make a convenient and quick foot measurement. A method is proposed by combining the image measurement with the deep neural network. Based on the physiological structure analysis of foot, key points are extracted and the measurement parameters are defined. During the key point detection of foot, the activation function and loss function of DAN (Deep Alignment Network) model is optimized, and a data acquisition method is defined based on the handheld camera. Foot key points are detected, and main parameters are measured. Experimental results show that collecting data based on handheld camera can conveniently measure foot parameters and the precision is high.

Recommended Citation

Min, Shi; Yao, Hanqin; Li, Chunpeng; and Chen, Liangchen (2020) "Foot Measurement Based on Deep Alignment Network," Journal of System Simulation: Vol. 32: Iss. 7, Article 7.
DOI: 10.16182/j.issn1004731x.joss.19-VR0467
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss7/7

First Page

1267

Revised Date

2019-11-04

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-VR0467

Last Page

1278

CLC

TP391.9

Recommended Citation

Shi Min, Yao Hanqin, Li Chunpeng, Chen Liangchen. Foot Measurement Based on Deep Alignment Network[J]. Journal of System Simulation, 2020, 32(7): 1267-1278.

DOI

10.16182/j.issn1004731x.joss.19-VR0467