  The vision of the internet of things (IoT) is a reality now. IoT devices are
getting cheaper, smaller. They are becoming more and more computationally and
energy-efficient. The global market of IoT-based video analytics has seen
significant growth in recent years and it is expected to be a growing market
segment. For any IoT-based video analytics application, few key points
required, such as cost-effectiveness, widespread use, flexible design, accurate
scene detection, reusability of the framework. Video-based smart doorbell
system is one such application domain for video analytics where many commercial
offerings are available in the consumer market. However, such existing
offerings are costly, monolithic, and proprietary. Also, there will be a
trade-off between accuracy and portability. To address the foreseen problems,
I'm proposing a distributed framework for video analytics with a use case of a
smart doorbell system. The proposed framework uses AWS cloud services as a base
platform and to meet the price affordability constraint, the system was
implemented on affordable Raspberry Pi. The smart doorbell will be able to
recognize the known/unknown person with at most accuracy. The smart doorbell
system is also having additional detection functionalities such as harmful
weapon detection, noteworthy vehicle detection, animal/pet detection. An iOS
application is specifically developed for this implementation which can receive
the notification from the smart doorbell in real-time. Finally, the paper also
mentions the classical approaches for video analytics, their feasibility in
implementing with this use-case, and comparative analysis in terms of accuracy
and time required to detect an object in the frame is carried out. Results
conclude that AWS cloud-based approach is worthy for this smart doorbell use
case.
