通过分析个体速度实现人群异常行为实时检测
VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis
- 结合头部检测与光流计算个人速度
- 分层聚类划分运动状态，百分位评分识别异常
- 适用于高密度人群场景，结果可解释性强
在拥挤场景中检测异常行为面临严重的人体遮挡和高度动态、依赖上下文的运动模式挑战。现有方法常难以适应不同人群密度，且缺乏可解释的异常指标。为此，我们提出VelocityNet，一种双管道框架，结合头部检测与密集光流，提取个体速度。分层聚类将这些速度划分为语义运动类别（静止、缓慢、正常、快速），并采用基于百分位的异常评分系统衡量对正常模式的偏离。实验表明，该框架在高密度人群环境中能实时检测多种异常运动模式。
原文摘要 · Abstract (English)
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.
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