Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.15773 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915086442954752 |
|---|---|
| author | Akhlaq, Filza Arshad, Alina Hayati, Muhammad Yehya Shamsi, Jawwad A. Khan, Muhammad Burhan |
| author_facet | Akhlaq, Filza Arshad, Alina Hayati, Muhammad Yehya Shamsi, Jawwad A. Khan, Muhammad Burhan |
| contents | Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptable systems that can interpret context to trigger appropriate responses. This paper addresses these challenges by proposing a versatile detection system for smart city applications, offering a solution tested across traffic and fire detection scenarios. Our contributions include an analysis of detection requirements and the development of a system adaptable to diverse applications, advancing automated surveillance for smart cities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Context-Aware Detection of Mixed Critical Events using Video Classification Akhlaq, Filza Arshad, Alina Hayati, Muhammad Yehya Shamsi, Jawwad A. Khan, Muhammad Burhan Computer Vision and Pattern Recognition Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptable systems that can interpret context to trigger appropriate responses. This paper addresses these challenges by proposing a versatile detection system for smart city applications, offering a solution tested across traffic and fire detection scenarios. Our contributions include an analysis of detection requirements and the development of a system adaptable to diverse applications, advancing automated surveillance for smart cities. |
| title | Context-Aware Detection of Mixed Critical Events using Video Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.15773 |