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Unmanned aerial vehicles for human detection and recognition using neural-network model.

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  • 1Faculty of Computer Science and AI, Air University, Islamabad, Pakistan.

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Summary

This study introduces a novel method for human action recognition in drone-recorded videos. The approach achieves high accuracy by processing RGB frames, detecting human bodies with YOLOv9, and analyzing skeleton data with a deep neural network.

Keywords:
convolutional neural network (CNNs)decision-making processesneural networksequential data processingunmanned aerial vehiclesunmanned aerial vehicles neural network

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human action recognition is vital for machine understanding of behavior.
  • Drone-based RGB videos present challenges like dynamic backgrounds, motion blur, and occlusions.
  • Existing methods struggle with the complexities of aerial footage.

Purpose of the Study:

  • To develop a robust method for human action recognition in challenging drone-recorded RGB videos.
  • To improve the accuracy and reliability of action recognition systems in real-world scenarios.
  • To address the limitations of current approaches in handling diverse human movements and video conditions.

Main Methods:

  • Video frame segmentation and preprocessing to enhance foreground object visibility.
  • Human body detection using the YOLOv9 algorithm.
  • Skeleton extraction, feature engineering (positions, angles, distances), and 3D point cloud generation.
  • Data optimization with Kernel Discriminant Analysis (KDA) and classification via a deep Convolutional Neural Network (CNN).

Main Results:

  • The proposed model achieved action recognition accuracies of 0.68 on UAV-Human, 0.75 on UCF, and 0.83 on Drone-Action datasets.
  • Effective handling of challenges such as motion blur, occlusions, and varying viewpoints.
  • Demonstrated superior performance compared to existing methods on benchmark datasets.

Conclusions:

  • The developed method offers a significant advancement in human action recognition from drone footage.
  • The approach is effective in overcoming common challenges associated with aerial video analysis.
  • This work paves the way for more sophisticated human-aware systems in surveillance, robotics, and entertainment.