Deep Joint Transmission-Recognition for Multi-View Cameras

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ID: 283443
2020
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Abstract
We propose joint transmission-recognition schemes for efficient inference at the wireless edge. Motivated by the surveillance applications with wireless cameras, we consider the person classification task over a wireless channel carried out by multi-view cameras operating as edge devices. We introduce deep neural network (DNN) based compression schemes which incorporate digital (separate) transmission and joint source-channel coding (JSCC) methods. We evaluate the proposed device-edge communication schemes under different channel SNRs, bandwidth and power constraints. We show that the JSCC schemes not only improve the end-to-end accuracy but also simplify the encoding process and provide graceful degradation with channel quality.
Reference Key
jankowski2020deep Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ezgi Ozyilkan; Mikolaj Jankowski
Journal arXiv
Year 2020
DOI
DOI not found
URL
http://arxiv.org/abs/2011.01902v1
Keywords
cs.lg cs.cv cs.it math.it

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