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Improved Action Recognition with Separable Spatio-Temporal Attention Using Alternative Skeletal and Video

Pau Climent-Pérez1, Francisco Florez-Revuelta1

  • 1Department of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain.

Sensors (Basel, Switzerland)
|February 5, 2021
PubMed
Summary

This study enhances activity recognition from video for assisted living by introducing novel data normalization techniques. These methods improve performance on realistic datasets, advancing behavior understanding and lifelogging capabilities.

Keywords:
action recognitionactive and assisted livingcomputer visiondeep learninginflated convolutional neural networksspatio-temporal attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Recognizing activities of daily living (ADLs) from video is crucial for active and assisted living technologies.
  • Current methods for behavior understanding and lifelogging have limitations in real-world scenarios.
  • Realistic datasets like Toyota Smarthomes necessitate improved action recognition algorithms.

Purpose of the Study:

  • To enhance action recognition accuracy for activities of daily living (ADLs) using video data.
  • To introduce novel data normalization techniques for skeletal pose and RGB data.
  • To improve upon existing baseline results and outperform state-of-the-art methods in ADL recognition.

Main Methods:

  • Utilized a separable spatio-temporal attention network architecture.
  • Introduced a view-invariant normalization method for skeletal pose data.
  • Applied full activity cropping for RGB data processing.

Main Results:

  • Achieved a 9.5% improvement in results on cross-subject experiments.
  • Outperformed state-of-the-art techniques using original, unmodified skeletal data.
  • Demonstrated enhanced performance on realistic datasets for ADL recognition.

Conclusions:

  • The proposed normalization techniques significantly improve action recognition for assisted living.
  • The methods offer a robust approach for behavior understanding and lifelogging applications.
  • Advances in action recognition contribute to more effective active and assisted living solutions.