Detecting Contextual Anomalies by Discovering Consistent Spatial Regions
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915104275038208 |
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| author | Yang, Zhengye Radke, Richard J. |
| author_facet | Yang, Zhengye Radke, Richard J. |
| contents | We describe a method for modeling spatial context to enable video anomaly detection. The main idea is to discover regions that share similar object-level activities by clustering joint object attributes using Gaussian mixture models. We demonstrate that this straightforward approach, using orders of magnitude fewer parameters than competing models, achieves state-of-the-art performance in the challenging spatial-context-dependent Street Scene dataset. As a side benefit, the high-resolution discovered regions learned by the model also provide explainable normalcy maps for human operators without the need for any pre-trained segmentation model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Contextual Anomalies by Discovering Consistent Spatial Regions Yang, Zhengye Radke, Richard J. Computer Vision and Pattern Recognition Artificial Intelligence We describe a method for modeling spatial context to enable video anomaly detection. The main idea is to discover regions that share similar object-level activities by clustering joint object attributes using Gaussian mixture models. We demonstrate that this straightforward approach, using orders of magnitude fewer parameters than competing models, achieves state-of-the-art performance in the challenging spatial-context-dependent Street Scene dataset. As a side benefit, the high-resolution discovered regions learned by the model also provide explainable normalcy maps for human operators without the need for any pre-trained segmentation model. |
| title | Detecting Contextual Anomalies by Discovering Consistent Spatial Regions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.08470 |