  The concept of the Internet of Things (IoT) is a reality now. This paradigm
shift has caught everyones attention in a large class of applications,
including IoT-based video analytics using smart doorbells. Due to its growing
application segments, various efforts exist in scientific literature and many
video-based doorbell solutions are commercially available in the market.
However, contemporary offerings are bespoke, offering limited composability and
reusability of a smart doorbell framework. Second, they are monolithic and
proprietary, which means that the implementation details remain hidden from the
users. We believe that a transparent design can greatly aid in the development
of a smart doorbell, enabling its use in multiple application domains.
  To address the above-mentioned challenges, we propose a distributed framework
to orchestrate video analytics across Edge and Cloud resources. We investigate
trade-offs in the distribution of different software components over a
bespoke/full system, where components over Edge and Cloud are treated
generically. This paper evaluates the proposed framework as well as the
state-of-the-art models and presents comparative analysis of them on various
metrics (such as overall model accuracy, latency, memory, and CPU usage). The
evaluation result demonstrates our intuition very well, showcasing that the
AWS-based approach exhibits reasonably high object-detection accuracy, low
memory, and CPU usage when compared to the state-of-the-art approaches, but
high latency.
