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Online Video Anomaly Detection.

Yuxing Zhang1, Jinchen Song1, Yuehan Jiang1

  • 1School of Information Science and Technology, Nantong University, Nantong 226019, China.

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Summary
This summary is machine-generated.

This study reviews online video anomaly detection methods, crucial for real-time surveillance. It categorizes techniques, analyzes datasets, and evaluates algorithms for improved abnormal event detection.

Keywords:
online video anomaly detectionreal timevideo surveillance

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Surveillance Technology

Background:

  • Increasing popularity of video surveillance necessitates timely detection of abnormal events.
  • Real-time, automatic, and accurate anomaly detection is a primary objective for video surveillance systems.
  • Significant research has focused on online video anomaly detection to meet these demands.

Purpose of the Study:

  • To provide a comprehensive overview of online video anomaly detection research.
  • To classify and explain various online video anomaly detection methodologies.
  • To analyze common datasets and evaluate current algorithm performance.

Main Methods:

  • Review and classification of existing online video anomaly detection research.
  • Explanation of the core concepts and characteristics of different detection methods.
  • Comparative analysis of mainstream algorithms using standard datasets and evaluation metrics.

Main Results:

  • Categorization of online video anomaly detection methods.
  • Summary of commonly used datasets in the field.
  • Performance comparison of current algorithms on benchmark datasets.

Conclusions:

  • The paper consolidates current knowledge in online video anomaly detection.
  • It highlights key datasets and algorithm performances.
  • Future research trends in this domain are identified.