VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866911223239409664 |
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| author | AlGhamdi, Fatima Alharbi, Omar Aldwyish, Abdullah Aljadaany, Raied Khan, Muhammad Kamran J Alamri, Huda |
| author_facet | AlGhamdi, Fatima Alharbi, Omar Aldwyish, Abdullah Aljadaany, Raied Khan, Muhammad Kamran J Alamri, Huda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis AlGhamdi, Fatima Alharbi, Omar Aldwyish, Abdullah Aljadaany, Raied Khan, Muhammad Kamran J Alamri, Huda Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2510.18187 |