  While Deep Neural Network (DNN) models have provided remarkable advances in
machine vision capabilities, their high computational complexity and model
sizes present a formidable roadblock to deployment in AIoT-based sensing
applications. In this paper, we propose a novel paradigm by which peer nodes in
a network can collaborate to improve their accuracy on person detection, an
exemplar machine vision task. The proposed methodology requires no re-training
of the DNNs and incurs minimal processing latency as it extracts scene
summaries from the collaborators and injects back into DNNs of the reference
cameras, on-the-fly. Early results show promise with improvements in recall as
high as 10% with a single collaborator, on benchmark datasets.
