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NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector by Pedro Machado; João Filipe Ferreira; Andreas Oikonomou; T. M. McGinnity is a scholarly article available to read on EtoBox.

What is NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector about?

Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, which still represent 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 720 × 480 at 28.06 Frames Per Second (fps) and 720 × 480 at 28.71 fps, respective

Author

Pedro Machado; João Filipe Ferreira; Andreas Oikonomou; T. M. McGinnity

Publisher

ACM

Published

2023

Language

EN