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Human Activity Recognition by Sequences of Skeleton Features.

Heilym Ramirez1, Sergio A Velastin2,3, Paulo Aguayo1

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This study introduces a vision-based human activity recognition system using skeleton pose estimation. It effectively detects multiple people

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Area of Science:

  • Computer Vision
  • Human Activity Recognition
  • Artificial Intelligence

Background:

  • Wearable sensors for fall detection have limitations, including discomfort and restricted use in open spaces or with unfamiliar individuals.
  • Vision-based approaches offer advantages by eliminating the need for body-worn sensors, enabling application in diverse environments and with unknown subjects.

Purpose of the Study:

  • To develop a vision-based algorithm for human activity recognition, specifically focusing on fall detection.
  • To leverage human skeleton pose estimation as a primary feature extraction method for enhanced activity detection.

Main Methods:

  • Utilized human skeleton pose estimation to extract features from video camera images for activity recognition.
  • Developed an algorithm capable of detecting activities of multiple individuals within the same scene.
  • Implemented multi-frame activity classification to accurately identify activities requiring more than one video frame.

Main Results:

  • The algorithm successfully performs human activity recognition using skeleton pose estimation.
  • Demonstrated the capability to detect activities of multiple people simultaneously.
  • Validated the algorithm's effectiveness in classifying multi-frame activities.

Conclusions:

  • Human skeleton pose estimation is an effective feature extraction technique for vision-based activity recognition.
  • The proposed algorithm offers a robust solution for detecting multiple people's activities in various settings.
  • The method shows promise for applications like elderly fall detection, enhancing safety and independence.