{"ID":2851974,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18187","arxiv_id":"2510.18187","title":"VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis","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.","short_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 du...","url_abs":"https://arxiv.org/abs/2510.18187","url_pdf":"https://arxiv.org/pdf/2510.18187v1","authors":"[\"Fatima AlGhamdi\",\"Omar Alharbi\",\"Abdullah Aldwyish\",\"Raied Aljadaany\",\"Muhammad Kamran J Khan\",\"Huda Alamri\"]","published":"2025-10-21T00:26:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}