Deep Learning Based Semi-Automatic Labeling System for Human Images

doi:10.13190/j.jbupt.2020-181

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (1): 104-109.doi: 10.13190/j.jbupt.2020-181

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Deep Learning Based Semi-Automatic Labeling System for Human Images

GAO Hui¹, ZHANG Ji-wei¹, LAI Yang², WANG Wen-dong³

1. School of Computer Science(National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Systems Engineering Research Institute of China State Shipbuilding Corporation, Beijing 100036, China;
3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-09-23 Online:2021-02-28 Published:2021-09-30

Abstract

Abstract: In view of the problem that data labeling is too dependent on hardware and manual data labeling is inefficient,a semi-automatic labeling system for human images based on deep learning is proposed. By improving the algorithm,the system increases the number of key points of the human body for feature extraction and adds motion information constraints,which improves the accuracy of video staged annotation. Experiments that employs real data sets prove the feasibility of data labeling by deep learning algorithm,and using deep learning algorithms for semi-automatic labeling is faster and more accurate.

Key words: image labeling, semi-automatic, deep learning, human reconstruction

CLC Number:

TP29

GAO Hui, ZHANG Ji-wei, LAI Yang, WANG Wen-dong. Deep Learning Based Semi-Automatic Labeling System for Human Images[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 104-109.

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Deep Learning Based Semi-Automatic Labeling System for Human Images

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