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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Intelligent Video Analytics for Human Action Recognition: The State of Knowledge.

Marek Kulbacki1,2, Jakub Segen1,2, Zenon Chaczko2,3

  • 1Polish-Japanese Academy of Information Technology, 02-008 Warsaw, Poland.

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|May 13, 2023
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Summary
This summary is machine-generated.

This paper reviews intelligent video analytics and human action recognition methods. It explores techniques like deep learning and visual transformers, discussing challenges and edge AI for real-time analysis.

Keywords:
deep learning-based HARedge AIhuman activity recognitionintelligent video analyticspose-based HARspatio-temporal-based HARtracking-based HARvideo surveillancevisual transformers

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human action recognition is crucial for intelligent video analytics.
  • Existing methods include pose-based, tracking-based, spatio-temporal, and deep learning approaches.
  • Visual transformers represent a recent advancement in this field.

Purpose of the Study:

  • To provide a comprehensive overview of intelligent video analytics and human action recognition.
  • To discuss the current state-of-the-art techniques and their limitations.
  • To explore the potential of edge AI for real-time human action recognition.

Main Methods:

  • Review of pose-based, tracking-based, and spatio-temporal methods.
  • Analysis of deep learning-based approaches, including convolutional neural networks and recurrent neural networks.
  • Inclusion of visual transformers as a novel deep learning technique.
  • Discussion of edge AI architectures for efficient computation.

Main Results:

  • Detailed comparison of various human action recognition techniques.
  • Identification of challenges and limitations in current methods.
  • Highlighting the feasibility of edge AI for real-time applications.

Conclusions:

  • Human action recognition is a rapidly evolving field with diverse methodologies.
  • Deep learning and visual transformers offer promising avenues for improved accuracy.
  • Edge AI architectures are key to enabling real-time, on-device human action recognition.