论文标题
近芯片动态视力过滤，用于低频道的行人检测
Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection
论文作者
论文摘要
本文提出了一种使用动态视觉传感器（DVSS）的新型端到端系统，用于行人检测。我们针对多个传感器将数据传输到本地处理单元的应用程序，该应用程序执行检测算法。我们的系统由（i）一个近芯片事件过滤器组成，该滤镜可压缩并授予DVS的事件流，以及（ii）在低兼容边缘计算设备上运行的二进制神经网络（BNN）检测模块（在我们的情况下，STM32F4微控制器）。我们介绍系统体系结构，并在办公环境中提供端到端的行人检测实现。与传输原始事件流相比，我们的实施使变速箱尺寸最多减少了99.6％。我们系统中的平均数据包大小仅为1397位，而发送未压缩的DVS时间窗口需要307.2 kb。我们的检测器能够每450毫秒进行一次检测，总体测试F1得分为83％。我们系统的低带宽和能量特性使其非常适合物联网应用。
This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs). We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our system is composed of (i) a near-chip event filter that compresses and denoises the event stream from the DVS, and (ii) a Binary Neural Network (BNN) detection module that runs on a low-computation edge computing device (in our case a STM32F4 microcontroller). We present the system architecture and provide an end-to-end implementation for pedestrian detection in an office environment. Our implementation reduces transmission size by up to 99.6% compared to transmitting the raw event stream. The average packet size in our system is only 1397 bits, while 307.2 kb are required to send an uncompressed DVS time window. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%. The low bandwidth and energy properties of our system make it ideal for IoT applications.
