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Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition

Fatai Idowu Sadiq1,2, Ali Selamat1,3,4, Roliana Ibrahim1

  • 1Faculty of Engineering, School of Computing, UTM & Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia.

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Summary
This summary is machine-generated.

This study introduces an enhanced context-aware framework (EHCAF) for crowd monitoring, significantly improving accuracy and reducing false negatives using smartphone sensing. The new system achieves 99.1% accuracy with a 2.8% false negative rate.

Keywords:
accuracycontext-aware frameworkfalse negative rateindividual behavior estimationstatistical-based time-frequency domain and crowd condition

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Area of Science:

  • Computer Science
  • Engineering
  • Public Safety

Background:

  • Sensor technology enables real-time data monitoring for enhanced security.
  • Crowd condition monitoring benefits from sensor technology, but existing frameworks like the basic context-aware framework (BCF) suffer from low accuracy and high false negative rates (FNR).

Purpose of the Study:

  • To develop an enhanced context-aware framework (EHCAF) that improves accuracy and reduces FNR in crowd monitoring.
  • To leverage smartphone participatory sensing for more effective crowd condition assessment.

Main Methods:

  • Developed an enhanced context-aware framework (EHCAF).
  • Utilized smartphone participatory sensing for data collection.
  • Applied dimensionality reduction to statistical-based time-frequency domain (SBTFD) features.
  • Implemented enhanced individual behavior estimation (IBEenhcaf).

Main Results:

  • The EHCAF achieved 99.1% accuracy in crowd monitoring.
  • The FNR was reduced to 2.8% with the EHCAF.
  • Demonstrated significant improvement over the BCF's 92.0% accuracy and 31.3% FNR.

Conclusions:

  • The developed EHCAF offers a substantial improvement for crowd monitoring applications.
  • Smartphone participatory sensing combined with advanced feature reduction and behavior estimation enhances system performance.
  • The EHCAF provides a more reliable and accurate solution for real-time crowd condition assessment.