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arXiv · 2510.18187

VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis

Fatima AlGhamdi·Omar Alharbi·Abdullah Aldwyish·Raied Aljadaany·Muhammad Kamran J Khan·Huda Alamri
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Abstract

Detecting anomalies in crowded scenes is challenging due to severe inter-person occlusions and highly dynamic, context-dependent motion patterns. Existing approaches often struggle to adapt to varying crowd densities and lack interpretable anomaly indicators. To address these limitations, we introduce VelocityNet, a dual-pipeline framework that combines head detection and dense optical flow to extract person-specific velocities. Hierarchical clustering categorizes these velocities into semantic motion classes (halt, slow, normal, and fast), and a percentile-based anomaly scoring system measures deviations from learned normal patterns. Experiments demonstrate the effectiveness of our framework in real-time detection of diverse anomalous motion patterns within densely crowded environments.

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Fatima AlGhamdi, Omar Alharbi, Abdullah Aldwyish, Raied Aljadaany, Muhammad Kamran J Khan, Huda Alamri. 2025-10-21. VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis. https://arxiv.org/abs/2510.18187

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