  Vertebrate retinas are highly-efficient in processing trivial visual tasks
such as detecting moving objects, yet a complex challenges for modern
computers. In vertebrates, the detection of object motion is performed by
specialised retinal cells named Object Motion Sensitive Ganglion Cells
(OMS-GC). OMS-GC process continuous visual signals and generate spike patterns
that are post-processed by the Visual Cortex. Our previous Hybrid Sensitive
Motion Detector (HSMD) algorithm was the first hybrid algorithm to enhance
Background subtraction (BS) algorithms with a customised 3-layer Spiking Neural
Network (SNN) that generates OMS-GC spiking-like responses. In this work, we
present a Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) algorithm
that accelerates our HSMD algorithm using Field-Programmable Gate Arrays
(FPGAs). The NeuroHSMD was compared against the HSMD algorithm, using the same
2012 Change Detection (CDnet2012) and 2014 Change Detection (CDnet2014)
benchmark datasets. When tested against the CDnet2012 and CDnet2014 datasets,
NeuroHSMD performs object motion detection at 720x480 at 28.06 Frames Per
Second (fps) and 720x480 at 28.71 fps, respectively, with no degradation of
quality. Moreover, the NeuroHSMD proposed in this paper was completely
implemented in Open Computer Language (OpenCL) and therefore is easily
replicated in other devices such as Graphical Processing Units (GPUs) and
clusters of Central Processing Units (CPUs).
