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Anomaly Event Detection Algorithm of Single-person Households Fusing Vision, Activity, and LiDAR Sensors

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(6), pp.23-31
  • DOI : 10.9708/jksci.2022.27.06.023
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 19, 2022
  • Accepted : June 9, 2022
  • Published : June 30, 2022

Do-Hyeon Lee 1,  AHN JUN HO 1

1한국교통대학교

Accredited

ABSTRACT

Due to the recent outbreak of COVID-19 and an aging population and an increase in single-person households, the amount of time that household members spend doing various activities at home has increased significantly. In this study, we propose an algorithm for detecting anomalies in members of single-person households, including the elderly, based on the results of human movement and fall detection using an image sensor algorithm through home CCTV, an activity sensor algorithm using an acceleration sensor built into a smartphone, and a 2D LiDAR sensor-based LiDAR sensor algorithm. However, each single sensor-based algorithm has a disadvantage in that it is difficult to detect anomalies in a specific situation due to the limitations of the sensor. Accordingly, rather than using only a single sensor-based algorithm, we developed a fusion method that combines each algorithm to detect anomalies in various situations. We evaluated the performance of algorithms through the data collected by each sensor, and show that even in situations where only one algorithm cannot be used to detect accurate anomaly event through certain scenarios we can complement each other to efficiently detect accurate anomaly event.

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This paper was written with support from the National Research Foundation of Korea.
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