Internet of Things (IoT) Based Video Analytics: a use case of Smart Doorbell

TL;DR

This paper presents a distributed IoT-based video analytics framework for smart doorbells, combining cloud and affordable hardware to improve accuracy, flexibility, and cost-effectiveness in real-time person and object detection.

Contribution

It introduces a novel distributed framework utilizing AWS cloud and Raspberry Pi for smart doorbell video analytics, addressing cost, accuracy, and portability issues of existing solutions.

Findings

AWS cloud approach enhances detection accuracy and speed.

Framework successfully recognizes known and unknown persons.

Additional detection features include weapons, vehicles, and pets.

Abstract

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.