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).

翻译：脊椎动物的视网膜在处理诸如运动物体检测等简单视觉任务时表现出极高的效率，这对现代计算机而言却是一个复杂的挑战。在脊椎动物中，物体运动的检测由名为物体运动敏感神经节细胞（OMS-GC）的特化视网膜细胞执行。OMS-GC处理连续视觉信号并生成尖峰模式，这些模式随后由视觉皮层进一步处理。我们此前提出的混合敏感运动检测器（HSMD）算法是首个混合算法，它利用定制化的3层尖峰神经网络（SNN）增强背景减除（BS）算法，以生成类OMS-GC的尖峰响应。在本工作中，我们提出了一种神经形态混合敏感运动检测器（NeuroHSMD）算法，该算法利用现场可编程门阵列（FPGA）加速了之前的HSMD算法。使用相同的2012年变化检测基准数据集（CDnet2012）和2014年变化检测基准数据集（CDnet2014），将NeuroHSMD与HSMD算法进行了比较。在CDnet2012和CDnet2014数据集上测试时，NeuroHSMD能够分别以28.06帧/秒（fps）和28.71帧/秒的速度处理720×480分辨率的运动物体检测任务，且质量未下降。此外，本文提出的NeuroHSMD完全使用开放计算语言（OpenCL）实现，因此可轻松移植到其他设备，如图形处理单元（GPU）和中央处理单元（CPU）集群。