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<p>Identifying and preventing abnormal behaviour in public areas has become more critical, as the reporting cases of such issues are growing nowadays. This emphasises the need to detect such abnormal situations in public spaces. Artificial Intelligence advancements based on Deep Learning and Computer Vision have greatly aided in the automated identification of anomalous occurrences. The proposed model offers a complete approach for detecting and analysing abnormal behaviour using an optimised YOLO network, enhanced through Adam Optimization, histogram equalisation, and other refinement techniques. In order to identify abnoraml behaviour precisely, the system employs optimisation methods, which also help to increase the detection process’s accuracy. The program also tracks observed items over time to find abnormal behaviour patterns. An improved YOLO framework is used for human detection, and the system’s performance is measured in terms of its capacity to recognise and analyse abnormal behaviour. The model proposed provides a remarkable accuracy of 99.46% in identifying and analysing abnormal behaviour, making it applicable to various real-time applications.</p>

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Real-time abnormal behaviour detection using energy-efficient YOLO-based framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月6日

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  • Sreedevi R. Krishnan,
  • P. Amudha,
  • S. Sivakumari

摘要

Identifying and preventing abnormal behaviour in public areas has become more critical, as the reporting cases of such issues are growing nowadays. This emphasises the need to detect such abnormal situations in public spaces. Artificial Intelligence advancements based on Deep Learning and Computer Vision have greatly aided in the automated identification of anomalous occurrences. The proposed model offers a complete approach for detecting and analysing abnormal behaviour using an optimised YOLO network, enhanced through Adam Optimization, histogram equalisation, and other refinement techniques. In order to identify abnoraml behaviour precisely, the system employs optimisation methods, which also help to increase the detection process’s accuracy. The program also tracks observed items over time to find abnormal behaviour patterns. An improved YOLO framework is used for human detection, and the system’s performance is measured in terms of its capacity to recognise and analyse abnormal behaviour. The model proposed provides a remarkable accuracy of 99.46% in identifying and analysing abnormal behaviour, making it applicable to various real-time applications.