Earticle

초록

영어

Considering the aging of individuals with the increasing population, the demand for technologies that enable people to follow their daily lives unnoticed is increasing day by day. In this article, a high-performance solution to the fall and posture detection problem for CCD camera-based fall detection systems is provided. Fall detection was performed with images obtained with CCD cameras placed in different positions. Within the scope of the proposed method, two different pre-trained CNN structures were trained using two different camera images. Data fusion was applied to the high-level features obtained from these structures. Features that were fusion process applied to different classifiers were given as input and ensemble learning process was applied. Considering the performance metrics of the proposed method, it was predicted that promising results were obtained for fall detection.

목차

Abstract
1. Introduction
2. Materials
2.1. System and Dataset Description
3. Methodology
3.1. The Background in the Deep Learning Models
3.2. Deep Learning Models
3.3. Ensemble Learning
3.4. Proposed Method
4. Results and Discussion
5. Conclusions
Acknowledgement
6. References

키워드

저자

• Arzo Mohammed Mohammed Mahmood [ Suleyman Demirel University, Graduate School of Natural and Applied Sciences, Turkey ] Corresponding Author
• Gul Fatma Turker [ Suleyman Demirel University, Faculty of Engineering, Turkey ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  IJICTDC [International Journal of Information Communication Technology and Digital Convergence]

• 간기

  반년간

• pISSN

  2466-0094

• 수록기간

  2016~2026

• 십진분류

  KDC 300 DDC 303