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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.

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Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

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Related Experiment Video

Updated: Aug 7, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques.

Sireesha Gundu1, Hussain Syed1

  • 1School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, India.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

This study introduces a hybrid model for recognizing human activities from aerial surveillance footage. The novel approach achieves 99.25% accuracy, significantly improving upon existing methods for unmanned aerial vehicle (UAV) surveillance.

Keywords:
Bi-LSTMHOGMask-RCNNactivity recognitiondeep learning techniquesinstance segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Unmanned aerial vehicle (UAV) surveillance presents challenges in recognizing human activities from aerial perspectives.
  • Accurate activity recognition is crucial for various computer vision applications, including surveillance and video analysis.

Purpose of the Study:

  • To develop a robust hybrid model for single and multi-human activity recognition using aerial data.
  • To enhance the accuracy and efficiency of human activity classification in UAV surveillance.

Main Methods:

  • A hybrid model combining Histogram of Oriented Gradient (HOG), Mask-Regional Convolutional Neural Network (Mask-RCNN), and Bidirectional Long Short-Term Memory (Bi-LSTM) was employed.
  • HOG extracted patterns, Mask-RCNN generated feature maps, and Bi-LSTM captured temporal dynamics for action recognition.
  • The model utilized histogram gradient-based instance segmentation for improved feature extraction.

Main Results:

  • The proposed hybrid model achieved a high accuracy of 99.25% on the YouTube-Aerial dataset.
  • The Bi-LSTM component effectively reduced the error rate by leveraging its bidirectional processing.
  • The novel architecture demonstrated superior performance compared to state-of-the-art models in aerial activity recognition.

Conclusions:

  • The developed hybrid model offers a significant advancement in UAV-based human activity recognition.
  • The integration of HOG, Mask-RCNN, and Bi-LSTM provides an effective solution for complex aerial surveillance tasks.
  • This research contributes to more accurate and reliable human behavior analysis in aerial video data.