Deep Joint Transmission-Recognition for Multi-View Cameras

• URL: http://arxiv.org/abs/2011.01902v1
• Date: Tue, 3 Nov 2020 18:27:49 GMT
• Title: Deep Joint Transmission-Recognition for Multi-View Cameras
• Authors: Ezgi Ozyilkan, Mikolaj Jankowski
• Abstract summary: 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.
• Score: 1.6244541005112747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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