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.

中文翻译：

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用于低带宽行人检测的近芯片动态视觉滤波

本文提出了一种使用动态视觉传感器 (DVS) 进行行人检测的新型端到端系统。我们针对多个传感器将数据传输到执行检测算法的本地处理单元的应用程序。我们的系统由 (i) 一个近芯片事件过滤器组成，该过滤器对来自 DVS 的事件流进行压缩和降噪，以及 (ii) 一个在低计算边缘计算设备上运行的二进制神经网络 (BNN) 检测模块（在我们的案例是 STM32F4 微控制器）。我们展示了系统架构，并提供了办公环境中行人检测的端到端实现。与传输原始事件流相比，我们的实现将传输大小减少了 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 毫秒执行一次检测，整体测试 F1 得分为 83%。我们系统的低带宽和能量特性使其成为物联网应用的理想选择。