NeuroHSMD: neuromorphic hybrid spiking motion detector

Machado, P ORCID: https://orcid.org/0000-0003-1760-3871, Ferreira, JF ORCID: https://orcid.org/0000-0002-2510-2412, Oikonomou, A ORCID: https://orcid.org/0000-0002-5069-3971 and McGinnity, TM, 2023. NeuroHSMD: neuromorphic hybrid spiking motion detector. ACM Transactions on Reconfigurable Technology and Systems, 16 (3): 36. ISSN 1936-7406

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Abstract

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, 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).

Item Type:	Journal article
Publication Title:	ACM Transactions on Reconfigurable Technology and Systems
Creators:	Machado, P., Ferreira, J.F., Oikonomou, A. and McGinnity, T.M.
Publisher:	Association for Computing Machinery (ACM)
Date:	September 2023
Volume:	16
Number:	3
ISSN:	1936-7406
Identifiers:	Number Type 10.1145/3588318 DOI 1740077 Other
Rights:	© ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Reconfigurable Technology and Systems, 16(3), 36 http://dx.doi.org/10.1145/3588318
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	14 Mar 2023 12:22
Last Modified:	17 Jul 2023 12:58
URI:	https://irep.ntu.ac.uk/id/eprint/48506

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