TL;DR
This review aims to present a comprehensive framework for analyzing anomalous crowd behavior using conventional machine learning (ML) (non-deep learning (DL) techniques, which continue to be efficient and applicable for real-time and resource-limited surveillance contexts.
This study presents a hybrid deep learning model that enables autonomous detection and spatial analysis of anomalous behavior in crowded scenes to enhance safety in public spaces. Existing anomaly detection approaches often focus solely on event classification and provide limited information about the spatial distribut...
Amneen is a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV) to track crowd density in public places and prevent dangerous situations before they occur, and predicts congestion before it occurs.
A ResidualF-Lenet for detecting abnormal events in surveillance videos is designed, which integrates Residual Network with Lenet and achieved improved levels of accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) as 91.681, 90.740, and 92.802%, respectively.
Qualitative and quantitative results confirm that modeling normal action semantics provides a robust and scalable solution for real-world surveillance anomaly detection.
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