review · IEEE Access
Deep learning and expanding volumes of video data increasingly allow the automation of complex visual tasks previously dependent on human intervention. Edge intelligence combines edge computing with artificial intelligence, enabling resource-constrained Internet of Things devices to offload demanding computational workloads to local network edge servers rather than the cloud. This architecture delivers significant bandwidth savings and reduces latency for real-time video analytics. An assessment of current Edge AI technologies highlights diverse implementations across smart city environments. Relevant domains include public security and surveillance, urban transportation and traffic management, healthcare, education, sports, and entertainment. In addition to reviewing underlying artificial intelligence models and privacy-preserving methods, the work identifies core operational challenges and open research questions. These findings outline the current technological landscape and functional scope of decentralised visual processing systems for urban infrastructure.
Smart cities generate vast amounts of video data that can overwhelm network bandwidth and introduce unacceptable delays when sent to distant cloud servers. Processing video locally on edge servers enables immediate, automated decision-making for traffic control and public safety while maintaining user privacy. This helps planners and technologists build responsive urban systems that can handle real-time demands efficiently.
The review addresses applications relevant to municipal authorities, transport operators, and surveillance technology providers seeking lower latency and reduced bandwidth costs. It examines solutions in security, traffic flow, and healthcare. Because the work is a systematic review synthesising existing models, privacy techniques, and outstanding technical challenges, it represents early-stage conceptual framing rather than a tested commercial system ready for direct deployment.
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Video analytics with deep learning techniques has generated immense interest in academia and industry, captivating minds with its transformative potential. Deep learning techniques and the deluge of video data enable the mechanization of tasks that were once the exclusive domain of human effort. Furthermore, edge intelligence is emerging as an interdisciplinary technology that drives the fusion of edge computing and artificial intelligence (AI). Edge computing allows the Internet of Things (IoT) devices with limited resources to offload their compute-intensive AI applications to the network edge servers for execution. Specifically, AI workloads for video analytics can be moved to the network edge from the cloud, providing improved latency and bandwidth savings, among other benefits. This article reviews current technologies used in Edge AI-assisted video analytics in smart cities. It examines the various artificial intelligence models and privacy-preserving techniques used in edge video analytics. It identifies the various applications of video analytics in smart cities, including security and surveillance, transportation and traffic management, healthcare, education, sports and entertainment, and many more. Besides, it highlights the challenges of edge video analysis and open research issues. It is expected that this review will be valuable for researchers, engineers, and decision-makers who want to understand the landscape and scale of edge video analytics in smart cities.
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DOI: 10.1109/access.2023.3300658
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