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Follower: A Novel Self-Deployable Action Recognition Framework.

Xu Yang1,2, Dongjingdian Liu2, Jing Liu2

  • 1China Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, China.

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Summary
This summary is machine-generated.

This study introduces FOLLOWER, a novel framework for self-deployable action recognition. It uses a "fingerprint" library built from minimal user data to achieve high accuracy in recognizing actions from videos.

Keywords:
action recognitiondynamic time planninghuman pose estimationtemplate matching

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning for action recognition requires extensive labeled data, limiting its practical application.
  • Existing methods often lack universality due to dataset dependency.

Purpose of the Study:

  • To propose FOLLOWER, a self-deployable framework for ubiquitous action recognition.
  • To enable users to bootstrap and deploy action recognition services with minimal data.
  • To overcome the universality limitations of traditional deep learning approaches.

Main Methods:

  • Developed a novel self-deployable ubiquitous action recognition framework named FOLLOWER.
  • Constructed an action "fingerprint" library using a small set of user-defined sample actions.
  • Designed a pose action normalized feature extraction method based on 3D pose sequences.
  • Implemented a recognition process involving motion detection, action filtering, and adaptive template matching.

Main Results:

  • Achieved high recognition accuracy of 96.74% on a custom action video dataset with human pose annotation.
  • Demonstrated the effectiveness of the FOLLOWER framework in self-deployable action recognition.
  • Validated the approach for action recognition based on pose estimation.

Conclusions:

  • FOLLOWER offers an effective solution for self-deployable action recognition, reducing reliance on large labeled datasets.
  • The proposed method enables user-driven deployment of action recognition services.
  • The framework shows strong potential for real-world applications requiring adaptable and efficient action recognition.