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Session Ⅰ : Artificial Intelligence

Human Pose Detection Methodologies for Better Posture

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 8th International Conference on Next Generation Computing 2022 (2022.10)바로가기
  • 페이지
    pp.39-41
  • 저자
    Yonghyeon Cho, DongHee Han, JuHoon Park
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A419733

원문정보

초록

영어
Often most of the modern human people are suffering from a long time of working or studying on the stationary pose. Subsequently, the health of our life is highly threatened to be exacerbated by chronic orthopedic diseases. In order to solve this social problem, we suggest pose detection that can have the people who have deleterious postures be notified. By using nowadays advanced computer vision techniques, in this paper we suggest the posture recognition module to enhance our quality of life. While most posture recognition recognizes only one person's posture, we made our pipeline to perform posture recognition for multiple people through images obtained through a single camera. One of the big problems in measuring people's postures is that it is necessary to distinguish the various body structures and postures of people. For this, posture images and labeling of various people are required. We created pose images of people of various body types through images of a small number of people through skeleton-based coordinates augmentation. We made a posture classifier using various models and observed the improvement of augmentation performance for each model. Through this, we found that the postures of various people can be measured using a relatively small data set. In particular, for deep-learning models that require a lot of data, generalization performance was greatly improved.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. DATASET DESCRIPTION
A. Hand Made Dataset
B. Train and Test Dataset Splits
IV. METHODOLOGY
A. Skeleton Coordinates
B. Our Pipeline for Posture-Recognition
C. Rule-Based Method
D. Feature Extraction
E. AI-Based Classification
V. EVALUATION
A. Comparison of Performance between AI Methods
B. Data Augmentation
C. Effectiveness of Data Augmentation
VI. CONCLUSION
VII. FUTURE WORKS
REFERENCES

키워드

computer vision machine learning deep neural network data augmentation

저자

  • Yonghyeon Cho [ Dept. of Computer Science and Engineering Sogang University Seoul, Republic of Korea ] Corresponding Author
  • DongHee Han [ Dept. of Electronic Engineering Sogang University Seoul, Republic of Korea ]
  • JuHoon Park [ Dept. of Computer Science and Engineering Sogang University Seoul, Republic of Korea ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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