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.
Abstract (translated)
在拥挤场景中检测异常行为具有挑战性，因为存在严重的人员遮挡以及高度动态且依赖于上下文的运动模式。现有的方法往往难以适应不同的人群密度，并缺乏可解释的异常指标。为了解决这些限制，我们引入了VelocityNet，这是一种双管道框架，结合头部检测和密集光流来提取个人特定的速度信息。通过层次聚类将这些速度分类为语义运动类别（停止、慢速、正常和快速），并使用基于百分位数的方法来衡量与学习到的正常模式之间的偏差。实验表明，我们的框架能够在高密度拥挤环境中实时检测出各种异常运动模式，证明了其有效性。
URL
https://arxiv.org/abs/2510.18187
