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Spatio-Temporal Features in Action Recognition Using 3D Skeletal Joints.

Mihai Trăscău1, Mihai Nan2, Adina Magda Florea3

  • 1Faculty of Automatic Control and Computers, University Politehnica Bucharest, București 060042, Romania. mihai.trascau@cti.pub.ro.

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|January 24, 2019
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Summary
This summary is machine-generated.

This study enhances action recognition for Ambient Assisted Living (AAL) systems by improving the interpretation of 3D skeleton data. Novel spatial and temporal approaches achieve state-of-the-art accuracy in recognizing human actions.

Keywords:
AALAmIaction recognition

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Robust action recognition is crucial for Ambient Assisted Living (AAL) systems.
  • 3D skeleton joints from depth images (e.g., time-of-flight cameras) are effective for action recognition.
  • Skeletal data, despite appearing information-scarce, offers a robust representation for human actions.

Purpose of the Study:

  • To explore improved interpretations of spatial and temporal dimensions in action sequences.
  • To enhance the accuracy of action recognition using skeletal data.
  • To propose a novel architecture for action recognition.

Main Methods:

  • Extraction of 3D skeleton joints from depth images using time-of-flight (ToF) cameras.
  • Investigating different spatial and temporal feature representations of skeletal data.
  • Developing a new architecture employing temporal convolutions.

Main Results:

  • Demonstrated that intuitive modifications to spatial and temporal interpretations improve accuracy.
  • Achieved results comparable to the current state-of-the-art in action recognition.
  • Validated the effectiveness of the proposed temporal convolution architecture.

Conclusions:

  • The proposed methods offer significant improvements in action recognition accuracy using skeletal data.
  • The findings contribute to more robust and effective Ambient Assisted Living (AAL) systems.
  • The developed architecture represents a promising advancement in skeletal-based action recognition.