Enabling collaborative video sensing at the edge through convolutional sharing

• Main Authors: KASTHURI JAYARAJAH, WANNIARACHCHIGE DHANUJA THARITH WANNIARACHCHI, MISRA, Archan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7152; https://ink.library.smu.edu.sg/context/sis_research/article/8155/viewcontent/2012.08643v1.pdf
• Institution: Singapore Management University

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.