Enabling Collaborative Video Sensing at the Edge through Convolutional Sharing

• URL: http://arxiv.org/abs/2012.08643v1
• Date: Thu, 3 Dec 2020 06:29:09 GMT
• Title: Enabling Collaborative Video Sensing at the Edge through Convolutional Sharing
• Authors: Kasthuri Jayarajah, Dhanuja Wanniarachchige, Archan Misra
• Abstract summary: We propose a novel paradigm by which peer nodes in a network can collaborate to improve their accuracy on person detection. Early results show promise with improvements in recall as high as 10% with a single collaborator.
• Score: 2.2488787113581923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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