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Computer Vision Performance Analysis for Smart Doorbell System With IoT and Edge Computing
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Author(s): Gaurang Raval (Institute of Technology, Nirma University, India), Shailesh Arya (Institute of Technology, Nirma University, India), Pankesh Patel (Pandit Deendayal Petroleum University, India), Sharada Valiveti (Institute of Technology, Nirma University, India), Riya Shah (Institute of Technology, Nirma University, India)and Saurin Parikh (Institute of Technology, Nirma University, India)
Copyright: 2025
Pages: 24
Source title:
Modern Advancements in Surveillance Systems and Technologies
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)
DOI: 10.4018/979-8-3693-6996-8.ch009
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Abstract
The Artificial Intelligence of Things (AIoT) includes machine learning applications, algorithms, hardware, and software. AIoT can be roughly classified into - vibration, voice, and vision. All of these have distinct workloads and demand scalable solutions. The focus of this work is on vision-based applications. The current offerings are expensive, inflexible, and exclusive. There is a trade-off between the precision and portability. To address these issues, a video analytics-based solution is proposed. It processes the smart doorbell data in real-time. The system is able to distinguish known/unknown people with high accuracy. It also detects animal/pet, harmful weapon, noteworthy vehicle, and package. Various approaches are applied for detection like cloud computing, IoT boards, classical computer vision. As part of this research, we wanted to collate contemporary video analytics with privacy, security, energy usage, and opacity with focus on Hardware/software cost, resource usage, accuracy, and latency. The approach best suitable for application development is thereby concluded.
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