The integration of Edge AI technology into real-time video surveillance systems has emerged as a transformative approach in enhancing security measures and optimizing resource usage. This paper explores the implications of deploying edge-based artificial intelligence for video surveillance, focusing on the benefits, challenges, and future directions. Edge AI refers to the processing of data at the network's edge, closer to the data source, which reduces latency, bandwidth consumption, and the dependency on centralized servers. This abstract discusses how edge AI enables faster analytics and decision-making for surveillance systems, improving detection accuracy and responsiveness in real-time applications. The challenges such as power constraints, hardware limitations, and the management of diverse edge devices are also addressed. Furthermore, the paper outlines potential solutions to these challenges and suggests innovative approaches for future development, emphasizing the importance of edge AI in creating efficient, secure, and smart surveillance systems.
Anderson, D. Edge AI for Real-Time Video Surveillance Systems. Information Sciences and Technological Innovations, 2021, 3, 24. https://doi.org/10.69610/j.isti.20211121
AMA Style
Anderson D. Edge AI for Real-Time Video Surveillance Systems. Information Sciences and Technological Innovations; 2021, 3(2):24. https://doi.org/10.69610/j.isti.20211121
Chicago/Turabian Style
Anderson, David 2021. "Edge AI for Real-Time Video Surveillance Systems" Information Sciences and Technological Innovations 3, no.2:24. https://doi.org/10.69610/j.isti.20211121
APA style
Anderson, D. (2021). Edge AI for Real-Time Video Surveillance Systems. Information Sciences and Technological Innovations, 3(2), 24. https://doi.org/10.69610/j.isti.20211121
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References
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