Publication Type
Working Paper
Version
publishedVersion
Publication Date
12-2020
Abstract
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.
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
First Page
1
Last Page
6
Identifier
10.48550/arXiv.2012.08643
Citation
KASTHURI JAYARAJAH; WANNIARACHCHIGE DHANUJA THARITH WANNIARACHCHI; and MISRA, Archan.
Enabling collaborative video sensing at the edge through convolutional sharing. (2020). 1-6.
Available at: https://ink.library.smu.edu.sg/sis_research/7152
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.48550/arXiv.2012.08643