🤖 AI Summary
Detecting anomalous behaviors in densely crowded scenes remains challenging due to severe occlusion and context-dependent motion patterns. To address this, we propose a dual-channel real-time detection framework. Our method jointly leverages head detection and dense optical flow estimation to compute individual velocities, feeding them into a two-branch deep network. We introduce a percentile-based anomaly scoring mechanism coupled with hierarchical clustering to enable interpretable, semantics-aware modeling of motion anomalies—such as stopping or slow walking. The framework adapts dynamically to varying crowd densities, significantly enhancing robustness in anomaly discrimination. Evaluated on multiple dense crowd benchmarks, our approach achieves an 8.2% improvement in mean Average Precision (mAP) and a 22.7% reduction in false positive rate, while maintaining real-time inference speed (>30 FPS). Additionally, it generates physically meaningful velocity-based anomaly heatmaps and category-level explanations, ensuring high accuracy, strong interpretability, and practical deployability.
📝 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.