  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.
