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Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy

Faisal Abdullah1, Yazeed Yasin Ghadi2, Munkhjargal Gochoo3

  • 1Department of Computer Science, Air University, Islamabad 44000, Pakistan.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces a new automated video surveillance system for crowd behavior analysis. The novel approach enhances human detection and tracking for improved public safety in crowded areas.

Keywords:
Jaccard similaritybat optimizationhuman crowd behavior (HCB)improved entropy (IE)multi-person countingparticles gradient motion (PGM)speeded up robust features (SURF)

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Surveillance Systems

Background:

  • Manual surveillance is inefficient for crowd control.
  • Automated systems face challenges in accurate human foreground extraction.
  • Robust feature extraction and classification are crucial for human crowd behavior (HCB) analysis.

Purpose of the Study:

  • To develop an automated video surveillance system for effective crowd management.
  • To improve human detection, multi-person tracking, and HCB interpretation.
  • To enhance public safety in crowded environments.

Main Methods:

  • A novel Particles Force Model for foreground extraction and multi-person tracking.
  • Fusion of global (contour extraction) and local (PGD, SURF) descriptors for HCB detection.
  • Bat optimization for feature selection and an improved entropy classifier for decision-making.

Main Results:

  • The proposed system accurately extracts human foregrounds and performs robust multi-person tracking.
  • Achieved higher accuracy rates compared to state-of-the-art methods on benchmark datasets (PETS2009, UMN).
  • Demonstrated superior performance in detecting and interpreting human crowd behavior.

Conclusions:

  • The developed system offers a promising solution for automated crowd supervision and disaster prevention.
  • Effective for deployment in public spaces like airports, malls, and train stations.
  • Enhances security and management of crowds through advanced video analytics.