VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis
This addresses the challenge of real-time anomaly detection in crowded environments for surveillance and safety applications, representing an incremental improvement over existing methods.
The paper tackled the problem of detecting anomalies in crowded scenes by introducing VelocityNet, a dual-pipeline framework that extracts person-specific velocities and uses hierarchical clustering and percentile-based scoring to identify deviations from normal motion patterns, achieving real-time detection of diverse anomalies in dense crowds.
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.