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Uncovering Abnormal Behavior Patterns from Mobility Trajectories.

Hao Wu1, Xuehua Tang2, Zhongyuan Wang1

  • 1National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel method using mobility trajectory data to detect abnormal behaviors and identify dangerous patterns like wandering or scouting. This approach aids in proactive security and crime prevention by analyzing spatiotemporal characteristics of movement.

Area of Science:

  • Computer Science
  • Criminology
  • Data Science

Background:

  • Criminal activity prevention requires understanding dangerous behaviors.
  • Personal trajectory data offers insights into spatiotemporal patterns of movement.
  • Identifying abnormal behavior trajectories is crucial for detecting potential criminal acts.

Purpose of the Study:

  • To propose a novel method for discovering abnormal behaviors and judging abnormal behavior patterns using mobility trajectory data.
  • To identify specific abnormal behavior patterns such as wandering, scouting, random walk, and trailing.
  • To enhance security and crime prevention through advanced trajectory analysis.

Main Methods:

  • Utilizing Long Short-Term Memory (LSTM) networks for personal trajectory feature extraction.
Keywords:
LSTM-based methodabnormal behavior patternmobility trajectoryspatiotemporal characteristic

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  • Applying K-means clustering to identify abnormal trajectories within large datasets.
  • Employing spatio-temporal feature matching to classify identified abnormal behavior patterns.

    • Main Results:

    • The proposed method enables rapid discovery of abnormal trajectories.
    • Effective judgment of abnormal behavior patterns based on extracted features.
    • Demonstrated utility of mobility trajectory data for security applications.

    Conclusions:

    • Trajectory-based abnormal behavior discovery is a viable method for security.
    • The approach effectively identifies and categorizes dangerous behavioral patterns.
    • This research contributes to proactive crime prevention strategies through data analysis.