November 19, 2025Open Access

Hybrid Ensemble Learning for Real-Time Crowd Behavior Analysis Using Optical Flow and Deep Motion Features

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Real-time crowd behavior recognition improves with ensemble learning techniques, enhancing response in critical situations.
Accuracy is further boosted by integrating optical flow with convolutional neural networks and advanced classifiers for detection.
Analysis of benchmark datasets confirms a significant increase in performance metrics compared to traditional methods and models.
This approach highlights the effectiveness of bridging machine learning techniques for practical applications in video surveillance.

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Implication

Hybrid ensemble learning improves crowd behavior recognition by integrating optical flow and deep motion features, pointing to enhanced surveillance outcomes.

Cite This Study

Mabrouk et al. (2025) studied this question.

synapsesocial.com/papers/6924f095c0ce034ddc350d53 https://doi.org/10.38124/ijisrt/25nov636

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Implication

Hybrid ensemble learning improves crowd behavior recognition by integrating optical flow and deep motion features, pointing to enhanced surveillance outcomes.

Hybrid Ensemble Learning for Real-Time Crowd Behavior Analysis Using Optical Flow and Deep Motion Features

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Discussion

Implication

Cite This Study

Hybrid Ensemble Learning for Real-Time Crowd Behavior Analysis Using Optical Flow and Deep Motion Features

Key Points

Discussion

Implication

Cite This Study

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