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Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition.

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  • 1Faculty of Automatic Control and Computers, Politehnica University of Bucharest, 060042 Bucharest, Romania.

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Summary
This summary is machine-generated.

This study presents a fast neural network for human action recognition, combining graph convolutional networks (GCN) and temporal convolutional networks (TCN). This approach enhances human-computer interaction and real-time assistance for social robots.

Keywords:
action recognitiondata augmentationfast convolutional modelgraph convolutional networksequence modelingtemporal convolutional network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Human action recognition is crucial for Ambient Intelligence and user assistance systems.
  • Effective human-computer interaction and real-time support from social robots depend on accurate action prediction.
  • High inference speed is a critical performance metric for real-time applications.

Purpose of the Study:

  • To introduce a novel neural network approach for human action recognition.
  • To achieve high performance in action recognition with significantly increased inference speed.
  • To develop a comprehensive pipeline for feature extraction and model training.

Main Methods:

  • A neural network architecture combining graph convolutional networks (GCN) and temporal convolutional networks (TCN).
  • A two-stage pipeline for extracting relevant geometric features, including data augmentation and preprocessing.
  • Experimental evaluation of the proposed model's performance and inference speed.

Main Results:

  • The proposed GCN-TCN model demonstrates a suitable approach for action recognition.
  • The solution achieves a good balance between recognition performance and high inference speed.
  • The integrated pipeline, including data augmentation and preprocessing, further boosts overall performance.

Conclusions:

  • The developed neural network approach effectively recognizes human actions with high speed.
  • The combination of GCN and TCN, along with a robust feature extraction pipeline, is a promising direction for real-time action recognition.
  • This work contributes to improved human-computer interaction and advanced capabilities for social robots.