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<p>In today’s fast moving era, specially in urban world, crowd anomaly detection and continuous crowd behaviour monitoring is necessary to ensure public safety. This study reviews and give comparison of deep learning techniques such as “convolutional neural network” and “auto encoder” based models which is used in crowd anomaly detection and analysis. CNN based frameworks like YOLO and hybrid CNN LSTM models works well in extraction of spatial features and enables high accuracy detection in real time. While, autoencoder architectures contains spatio temporal and memory augmented variants and learn common behavioural patterns without labelled anomalies. Comparative evaluations shows that CNNs gives fast inference and accurate localization while autoencoders perform best in identifying new or unknown events and handling the shortage of labelled datasets. Overall, Deep learning based crowd anomaly detection shows significant progress toward automated, smart and intelligent monitoring. However, some challenges like occlusion, domain shifts and privacy concerns will need to be solved. The study concludes that by integrating lightweight frameworks with multi modal sensing and ethical design can make robust, adaptive and privacy preserving crowd management systems. This brings the research closer to the real world deployment and secure public environment.</p>

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Intelligent Surveillance for Safer Public Spaces: A Systematic and Comparative Review of Deep Learning Approaches for Crowd Behavior Analysis and Anomaly Detection

  • 期刊论文

  • 发表日期：

    2026年6月21日

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  • Krunal Kayasth,
  • Meet Shah,
  • Santosh Satapathy

摘要

In today’s fast moving era, specially in urban world, crowd anomaly detection and continuous crowd behaviour monitoring is necessary to ensure public safety. This study reviews and give comparison of deep learning techniques such as “convolutional neural network” and “auto encoder” based models which is used in crowd anomaly detection and analysis. CNN based frameworks like YOLO and hybrid CNN LSTM models works well in extraction of spatial features and enables high accuracy detection in real time. While, autoencoder architectures contains spatio temporal and memory augmented variants and learn common behavioural patterns without labelled anomalies. Comparative evaluations shows that CNNs gives fast inference and accurate localization while autoencoders perform best in identifying new or unknown events and handling the shortage of labelled datasets. Overall, Deep learning based crowd anomaly detection shows significant progress toward automated, smart and intelligent monitoring. However, some challenges like occlusion, domain shifts and privacy concerns will need to be solved. The study concludes that by integrating lightweight frameworks with multi modal sensing and ethical design can make robust, adaptive and privacy preserving crowd management systems. This brings the research closer to the real world deployment and secure public environment.