论文标题
多视摄像机的深关节传输识别
Deep Joint Transmission-Recognition for Multi-View Cameras
论文作者
论文摘要
我们提出了联合传输识别方案，以在无线边缘有效推断。通过无线摄像机的监视应用程序的激励，我们考虑了通过作为边缘设备运行的多视频摄像头执行的无线通道上的人分类任务。我们介绍了基于深神经网络（DNN）的压缩方案，该方案结合了数字（单独的）传输和联合源通道编码（JSCC）方法。我们在不同的通道SNR，带宽和功率约束下评估了提议的设备边缘通信方案。我们表明，JSCC方案不仅提高了端到端的准确性，还可以简化编码过程，并以渠道质量提供优雅的降级。
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.
