Real time anomalies detection on video
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916449669349376 |
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| author | Poirier, Fabien |
| author_facet | Poirier, Fabien |
| contents | Nowadays, many places use security cameras. Unfortunately, when an incident occurs, these technologies are used to show past events. So it can be considered as a deterrence tool than a detection tool. In this article, we will propose a deep learning approach trying to solve this problematic. This approach uses convolutional models (CNN) to extract relevant characteristics linked to the video images, theses characteristics will form times series to be analyzed by LSTM / GRU models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real time anomalies detection on video Poirier, Fabien Computer Vision and Pattern Recognition Nowadays, many places use security cameras. Unfortunately, when an incident occurs, these technologies are used to show past events. So it can be considered as a deterrence tool than a detection tool. In this article, we will propose a deep learning approach trying to solve this problematic. This approach uses convolutional models (CNN) to extract relevant characteristics linked to the video images, theses characteristics will form times series to be analyzed by LSTM / GRU models. |
| title | Real time anomalies detection on video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.18051 |