Video surveillance in smart cities: current status, challenges & future directions

被引：0
作者：
Sharma H. [1 ]
Kanwal N. [1 ]
机构：
[1] Engineering, Punjabi University, Punjab, Patiala
来源：
Multimedia Tools and Applications | 2025年 / 84卷 / 16期
关键词：
Anomaly detection; Fire detection; Machine learning; Object detection; Smart city surveillance system;
D O I：
10.1007/s11042-024-19696-6
中图分类号：
学科分类号：
摘要：
People across the world aspire to settle in urban areas for better opportunities in career, education, and healthcare facilities. The increased proportion of people living in urban areas requires an improvement of smart habitat(s) robust enough to deal with the daily needs of citizens such as personal information management, security and surveillance to deal with anomalous activities in real-time. The present paper aims to provide an extensive review based on 213 research articles published from 2001 to 2023 highlighting various technologies for smart cities and intelligent video surveillance techniques, in order to: (i) Highlight the significance of smart city surveillance as well as the current research tendencies in this field. (ii) To present and explicate a standardized model of a smart city based on video surveillance. (iii) To analyze the current status and highlight challenges, and limitations of surveillance systems in different smart city applications. The paper outlines the critical role of video surveillance as a necessary feature of every subdomain of the smart city model. The fundamental element that defines the soon-to-come victorious period is the most recent technological developments for the detection of anomalous activity, fire, digital tampering, and objects, which are thoroughly examined in existing research papers and elucidated. The article further presents a well outlined bibliographic classification of state-of-the-art techniques. A comparison of the existing video surveillance datasets has also been thoroughly analyzed. Finally, the current work identifies major research challenges and future opportunities in this domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
引用
收藏
页码：15787 / 15832
页数：45
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