Dynamic vision sensors (DVS) are frame-free sensors
with an asynchronous variable-rate output that is ideal for hard
real-time dynamic vision applications under power and latency
constraints. Post-processing of the digital sensor output can
reduce sensor noise, extract low level features, and track objects
using simple algorithms that have previously been implemented
in software. In this paper we present an FPGA-based framework
for event-based processing that allows uncorrelated-event noise
removal and real-time tracking of multiple objects, with dynamic
capabilities to adapt itself to fast or slow and large or small
objects. This framework uses a new hardware platform based on
a Lattice FPGA which filters the sensor output and which then
transmits the results through a super-speed Cypress FX3 USB
microcontroller interface to a host computer. The packets of
events and timestamps are transmitted to the host computer at
rates of 10 Mega events per second. Experimental results are
presented that demonstrate a low latency of 10us for tracking
and computing the center of mass of a detected object.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
