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Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities.

Jing Zhang1, Ting Fan1, Ding Lang2

  • 1College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710600, China.

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Summary
This summary is machine-generated.

This study introduces novel algorithms for detecting pickpocketing individuals and identifying criminal groups using smart city data. These methods enhance urban safety by accurately identifying abnormal behaviors and group associations.

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CRS-Louvain algorithmIForest-FD algorithmcrowdsensingintelligent transportation

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Area of Science:

  • Computer Science
  • Urban Planning
  • Criminology

Background:

  • Smart cities generate massive sensing data for urban management and safety.
  • Detecting criminal individuals and identifying groups with similar behaviors from this data is challenging.

Purpose of the Study:

  • To develop algorithms for pickpocketing individual detection and pickpocketing group identification.
  • To improve urban safety and management through advanced data analysis.

Main Methods:

  • Proposed IForest-FD algorithm for individual detection using feature filtering and deep learning.
  • Developed CRS-Louvain algorithm for group identification based on feature similarity and graph analysis.

Main Results:

  • IForest-FD demonstrated superior performance in Precision, Recall, and F1-score for individual detection.
  • CRS-Louvain achieved better group division results compared to existing methods, as indicated by normalized mutual information.

Conclusions:

  • The proposed algorithms effectively address the challenges of detecting pickpocketing individuals and identifying criminal groups in smart cities.
  • These advancements contribute to comprehensive urban management and enhanced public safety.