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Skeleton Reconstruction Using Generative Adversarial Networks for Human Activity Recognition Under Occlusion.

Ioannis Vernikos1, Evaggelos Spyrou1

  • 1Department of Informatics and Telecommunications, University of Thessaly, 35100 Lamia, Greece.

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Summary
This summary is machine-generated.

This study introduces a novel Generative Adversarial Network (GAN) approach for human activity recognition using 3D skeleton data, effectively reconstructing occluded body parts to improve accuracy in real-world scenarios.

Keywords:
convolutional neural networksgenerative adversarial networkshuman activity recognitionocclusionreconstruction of skeleton joints

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Area of Science:

  • Computer Vision
  • Human Activity Recognition
  • Machine Learning

Background:

  • Human activity recognition from motion data is crucial but challenged by real-world factors like occlusion in camera-based systems.
  • Partial or complete occlusion of body parts significantly degrades the accuracy of human activity recognition.
  • Existing methods struggle with occluded data, limiting practical applications in unconstrained environments.

Purpose of the Study:

  • To develop a novel approach for robust human activity recognition under partial body occlusion (up to two body parts).
  • To leverage Generative Adversarial Networks (GANs) to reconstruct missing skeletal data caused by occlusion.
  • To improve recognition accuracy when specific body parts are consistently occluded during an activity.

Main Methods:

  • Modeling human motion using 3D skeletal joints.
  • Employing a Generative Adversarial Network (GAN) framework.
  • Utilizing a Convolutional Recurrent Neural Network (CRNN) as the generator to reconstruct occluded skeleton parts.
  • Using a Long Short-Term Memory (LSTM) network as the discriminator.

Main Results:

  • The proposed GAN-based approach effectively reconstructs occluded 3D skeleton joints.
  • Reconstruction significantly mitigates performance decline caused by occlusion, sometimes achieving near-perfect recognition.
  • The method demonstrates superior performance over previous works, with accuracy improvements ranging from 2.2% to 37.5% across different datasets and occlusion scenarios.

Conclusions:

  • Generative Adversarial Networks offer a powerful solution for handling partial occlusion in 3D human activity recognition.
  • The proposed CRNN-generator and LSTM-discriminator effectively reconstruct missing skeletal data, enhancing recognition robustness.
  • This approach significantly advances the state-of-the-art for activity recognition in challenging, real-world conditions with occluded body parts.