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.
@article{arxiv.2411.15773,
title = {Context-Aware Detection of Mixed Critical Events using Video Classification},
author = {Filza Akhlaq and Alina Arshad and Muhammad Yehya Hayati and Jawwad A. Shamsi and Muhammad Burhan Khan},
journal= {arXiv preprint arXiv:2411.15773},
year = {2025}
}
Comments
The results in this paper are old and outdated, as we are working more on the new updated data the results in this manuscript stand invalid. We will update the result on valid data and upload that one here. Thank you for your understanding