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Systematic review analyzes methods for complex event detection in video streams, suggesting improvements for future research.
Complex Event Detection (CED) in video streams involves numerous challenges such as object detection, tracking, spatio-temporal relationship identification, and event matching, which are often complicated by environmental variations, occlusions, and tracking losses. This systematic review presents an analysis of CED methods for video streams described in publications from 2012 to 2024, focusing on their effectiveness in addressing key challenges and identifying trends, research gaps, and future directions. A total of 92 studies were categorized into four main groups: training-based methods, object detection and spatio-temporal matching, multi-source solutions, and others. Each method's strengths, limitations, and applicability are discussed, providing an in-depth evaluation of their capabilities to support real-time video analysis and live camera feed applications. This review highlights the increasing demand for advanced CED techniques in sectors like security, safety, and surveillance and outlines the key opportunities for future research in this evolving field.
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Honarparvar et al. (2024) studied this question.
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