  As the number of installed cameras grows, so do the compute resources
required to process and analyze all the images captured by these cameras. Video
analytics enables new use cases, such as smart cities or autonomous driving. At
the same time, it urges service providers to install additional compute
resources to cope with the demand while the strict latency requirements push
compute towards the end of the network, forming a geographically distributed
and heterogeneous set of compute locations, shared and resource-constrained.
Such landscape (shared and distributed locations) forces us to design new
techniques that can optimize and distribute work among all available locations
and, ideally, make compute requirements grow sublinearly with respect to the
number of cameras installed. In this paper, we present FoMO (Focus on Moving
Objects). This method effectively optimizes multi-camera deployments by
preprocessing images for scenes, filtering the empty regions out, and composing
regions of interest from multiple cameras into a single image that serves as
input for a pre-trained object detection model. Results show that overall
system performance can be increased by 8x while accuracy improves 40% as a
by-product of the methodology, all using an off-the-shelf pre-trained model
with no additional training or fine-tuning.
