Documenting a Sensor-to-Command Bridge Before Claiming It: A Defensive Disclosure of an Open Situation-Picture Schema with Structural Anonymity Guarantees
Fatima AlGhamdi, Omar Al-Harbi, Abdullah Aldwyish, Raied Aljadaany, Muhammad Kamran J Khan, Huda Alamri · arXiv (Cornell University) · 2025
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.