NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, yet a complex task for modern computers. The detection of object motion is done by specialised retinal ganglion cells named Object-motion-sensitive ganglion cells (OMS-GC). OMS-GC process continuous signals and generate spike patterns that are post-processed by the Visual Cortex. The Neuromorphic Hybrid Spiking Motion Detector (NeuroHSMD) proposed in this work accelerates the HSMD algorithm using Field-Programmable Gate Arrays (FPGAs). The Hybrid Spiking Motion Detector (HSMD) algorithm was the first hybrid algorithm to enhance dynamic background subtraction (DBS) algorithms with a customised 3-layer spiking neural network (SNN) that generates OMS-GC spiking-like responses. The NeuroHSMD algorithm was compared against the HSMD algorithm, using the same 2012 change detection (CDnet2012) and 2014 change detection (CDnet2014) benchmark datasets. The results show that the NeuroHSMD has produced the same results as the HSMD algorithm in real-time without 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 Processor Units (GPUs) and clusters of Central Processor Units (CPUs).