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CamNuvem: A Robbery Dataset for Video Anomaly Detection.

Davi D de Paula1, Denis H P Salvadeo1, Darlan M N de Araujo1

  • 1IGCE-Institute of Geosciences and Exact Sciences, UNESP-São Paulo State University, Rio Claro 13506-900, SP, Brazil.

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Summary

This study introduces a new dataset for detecting robbery in surveillance videos, a challenging task in video anomaly detection. Current methods show limited success, highlighting a significant opportunity for improved robbery detection techniques.

Keywords:
activity recognitiondatasetdeep learninghuman behaviour analysisvideo anomaly detectionvideo surveillanceweakly supervised

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video surveillance anomaly detection seeks to identify unusual events automatically.
  • Crime detection, particularly robbery, is crucial but hampered by limited realistic data.
  • The UCF-Crime dataset has a small subset of robbery videos, necessitating new resources.

Purpose of the Study:

  • To address the scarcity of robbery data in video surveillance.
  • To create a new, weakly labeled dataset of real-world robbery surveillance videos.
  • To establish a benchmark for evaluating anomaly detection methods on robbery detection.

Main Methods:

  • A new dataset of 486 real-world robbery surveillance videos was compiled.
  • Three state-of-the-art video surveillance anomaly detection methods were adapted.
  • Performance was evaluated using Area Under the Curve (AUC) metrics.

Main Results:

  • The best adapted method achieved an AUC of 66.35% when considering only anomaly videos.
  • When including both anomaly and normal videos, the best method reached an AUC of 88.75%.
  • These results establish a benchmark for future research in robbery detection.

Conclusions:

  • Existing anomaly detection methods have limitations for effective robbery detection.
  • There is a substantial research gap and opportunity for developing novel approaches.
  • Improved methods are needed to enhance the accuracy and reliability of robbery detection in surveillance footage.