Earticle

초록

영어

Automatic human fall detection based on video monitoring plays an important role in protecting vulnerable people especially the seniors whose falls could cause severe injuries and need attentions from others immediately. In this paper, an automatic human fall detection method based on human motion tracking and shape aspect ratio in real-time video is proposed. While most existing methods detect falls in homes, the proposed method is suitable for both outdoor and indoor environments. The method first detects human objects in general environment, then tracks their motions, meanwhile calculating and recording the motion characteristics of each person. Comparing with the existing fall detection method using shape aspect ratio, the proposed method has advantages of employing the shape aspect ratio together with the moving speed and direction to better detect human falls, as well as being able to detect falls toward different directions. Experiment results demonstrate that the proposed method can effectively detect human falls in general environments including outdoor places.

목차

Abstract
 1. Introduction
 2. The Proposed Method
  2.1. Overview of the Fall Detection Method
  2.2. Human Object Detection
  2.3. Motion Tracking
 3. Fall Detection
  3.1. Shape Aspect Ratio and Fall Detection Criteria
  3.2. Using Shape Aspect Ratio for Fall Detection
 4. Experimental Results
 5. Conclusions
 References

키워드

저자

• Weidong Min [ School of Computer Science and Software Engineering, Tianjin Polytechnic University, China / School of Information Engineering, Nanchang University, China ]
• Longshu Wei [ School of Computer Science and Software Engineering, Tianjin Polytechnic University, China ]
• Qing Han [ School of Computer Science and Software Engineering, Tianjin Polytechnic University, China ]
• Yongzhen Ke [ School of Computer Science and Software Engineering, Tianjin Polytechnic University, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605