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A Systematic Review of Event-Matching Methods for Complex Event Detection in Video Streams.

Sepehr Honarparvar1, Zahra Bagheri Ashena1, Sara Saeedi2

  • 1Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

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|November 27, 2024
PubMed
Summary
This summary is machine-generated.

This review analyzes Complex Event Detection (CED) methods in video streams from 2012-2024. It categorizes 92 studies to identify trends and gaps in real-time video analysis for security and surveillance.

Keywords:
complex event detectionevent processingobject detection in videosvideo processing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Video Analytics

Background:

  • Complex Event Detection (CED) in video streams faces challenges like object detection, tracking, and environmental variations.
  • Existing methods struggle with occlusions and tracking losses, impacting real-time analysis.

Purpose of the Study:

  • To systematically review and analyze CED methods published between 2012 and 2024.
  • To evaluate the effectiveness of different CED approaches in addressing key challenges.
  • To identify trends, research gaps, and future directions in the field.

Main Methods:

  • A systematic literature review of 92 studies on CED methods for video streams.
  • Categorization of methods into four groups: training-based, object detection/spatio-temporal matching, multi-source solutions, and others.
  • In-depth discussion of each method's strengths, limitations, and applicability.

Main Results:

  • CED methods are increasingly vital for real-time video analysis in security, safety, and surveillance.
  • The review categorizes 92 studies, evaluating their performance against core CED challenges.
  • Identified trends show a growing demand for sophisticated CED techniques.

Conclusions:

  • Advanced CED techniques are crucial for enhancing live camera feed analysis and security applications.
  • Significant research gaps exist, particularly in robustly handling environmental variations and occlusions.
  • Future research should focus on developing more resilient and efficient CED methods for real-world scenarios.