Authors : Doaa Mabrouk; Manal A. Abdel-Fattah; Ahmed Taha

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/tupwzx4r

Scribd : https://tinyurl.com/m9t6ad8a

DOI : https://doi.org/10.38124/ijisrt/25nov636

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Abstract : Crowd Behavior analysis has become a crucial component of modern video surveillance systems, enabling automatic detection of abnormal events such as panic, congestion, and violence. Traditional approaches often fail to generalize under complex environmental conditions, while deep learning methods alone require large datasets and extensive computation. This paper proposes a hybrid ensemble learning framework that integrates optical flow–based motion features with deep motion representations extracted from convolutional neural networks (CNNs) to achieve real-time and robust crowd behavior recognition. The ensemble model combines Random Forests (RFs), Gradient Boosting (GB), and a lightweight CNN classifier via weighted voting. Experiments conducted on benchmark datasets, such as the UCSD Anomaly Detection Dataset and Violent Flows (VF), demonstrate that the proposed framework outperforms individual classifiers and state-of-the-art deep models in terms of accuracy, F1-score, and processing speed. The results confirm that ensemble learning effectively bridges the gap between handcrafted motion cues and deep spatio-temporal representations for practical surveillance applications.

Keywords : Crowd Behavior Analysis, Ensemble Learning, Optical Flow, Motion Analysis, Deep Learning, Surveillance Video, Anomaly Detection.

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