Outlier detection from vehicle trajectories to discover roaming events

Minxin Shen, Duen-Ren Liu*, Shi Han Shann

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Roaming, referring to the behavior of repeated observation of intended crime scenes before committing crimes, is a suspicious pattern often mentioned by experienced police investigators, but still remains a vague concept which needs to be well realized in video surveillance systems. This work first describes the scenario of roaming behaviors related to planned crimes, and then derives formal specifications for detecting suspicious roaming events from vehicle trajectories. Consequently, algorithms are designed for rapidly sorting out potential outliers and thoroughly examining their suspicious intention through circling activities, relative driving speed and time dispersion. Roaming trajectories and relevant trajectories are finally grouped into events and appropriately ranked. Furthermore, preliminary experiments and illustrative examples on synthetic data demonstrate the feasibility of our methods. The proposed approach enhances conventional vehicle plate analysis so that it becomes capable of discovering complex criminal behaviors and hence increasing investigation performance and decision quality.

Original languageEnglish
Pages (from-to)242-254
Number of pages13
JournalInformation Sciences
Volume294
DOIs
StatePublished - 10 Feb 2015

Keywords

  • Crime investigation
  • Outlier detection
  • Plate analysis
  • Trajectory mining

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