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Comparison between Recurrent Networks and Temporal Convolutional Networks Approaches for Skeleton-Based Action

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  • 1Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, RO-060042 Bucharest, Romania.

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This study enhances human action recognition using skeleton data by improving Temporal Convolutional Networks (TCNs). The proposed method achieves state-of-the-art results with faster inference speeds for skeleton-based action recognition.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human action recognition is crucial for applications like video surveillance and robotics.
  • Skeleton-based methods are popular for action recognition, often using Graph Convolutional Networks (GCNs), Temporal Convolutional Networks (TCNs), and Recurrent Neural Networks (RNNs).

Purpose of the Study:

  • To propose and validate improvements to existing skeleton-based action recognition methods.
  • To enhance the performance and inference speed of action recognition models.

Main Methods:

  • Exploration and comparison of spatial and temporal feature extraction techniques for action sequences.
  • Extension of Temporal Convolutional Network (TCN) units to incorporate spatial features.
  • Validation using a standard benchmark dataset for human action recognition.

Main Results:

  • The proposed TCN extension effectively utilizes spatial features for improved action recognition.
  • The method achieves results comparable to current state-of-the-art approaches.
  • A significant increase in inference speed was observed compared to existing methods.

Conclusions:

  • The enhanced TCN approach offers a promising direction for efficient and accurate skeleton-based human action recognition.
  • This work contributes to advancing real-time applications in areas like robotics and video analysis.