Deep Joint Transmission-Recognition for Multi-View Cameras
This work addresses the challenge of reliable inference in surveillance applications with wireless cameras, offering incremental improvements in communication efficiency.
The paper tackles the problem of efficient person classification from multi-view cameras at the wireless edge by proposing deep neural network-based compression schemes, including joint source-channel coding, which improves end-to-end accuracy and simplifies encoding under various channel conditions.
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.