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Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records

Published: 13 August 2016 Publication History

Abstract

Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in the urban area. Existing studies on the AFC data have primarily focused on identifying passengers' movement patterns. In this paper, however, we creatively leveraged such data for identifying thieves in the public transit systems. Indeed, stopping pickpockets in the public transit systems has been critical for improving passenger satisfaction and public safety. However, it is challenging to tell thieves from regular passengers in practice. To this end, we developed a suspect detection and surveillance system, which can identify pick-pocket suspects based on their daily transit records. Specifically, we first extracted a number of features from each passenger's daily activities in the transit systems. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who exhibit abnormal traveling behaviors. Experimental results demonstrated the effective- ness of our method. We also developed a prototype system with a user-friendly interface for the security personnel.

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Published In

cover image ACM Conferences
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2016
2176 pages
ISBN:9781450342322
DOI:10.1145/2939672
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 August 2016

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Author Tags

  1. anomaly detection
  2. automated fare collection
  3. mobility patterns
  4. public safety
  5. travel behaviors

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2024)Video-Based Fall Detection Using Human Pose and Constrained Generative Adversarial NetworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.3303258(1-1)Online publication date: 2024
  • (2023)Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart citiesMathematical Biosciences and Engineering10.3934/mbe.202361320:8(13777-13797)Online publication date: 2023
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