# NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

arXiv: https://arxiv.org/abs/2112.06102 · HTML: https://mlsanity.com/paper/2112.06102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 119 references that are in the map.
- First posted on arXiv 12 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Background Subtraction in Real Applications: Challenges, Current Models and Future Directions](https://mlsanity.com/paper/1901.03577/) (2019)
- [Spiking Neural Networks Hardware Implementations and Challenges: a Survey](https://mlsanity.com/paper/2005.01467/) (2020)
- [Moving Objects Detection with a Moving Camera: A Comprehensive Review](https://mlsanity.com/paper/2001.05238/) (2020)
- [Combining Background Subtraction Algorithms with Convolutional Neural Network](https://mlsanity.com/paper/1807.02080/) (2018)
- [Background Subtraction with Real-time Semantic Segmentation](https://mlsanity.com/paper/1811.10020/) (2018)
- [HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture](https://mlsanity.com/paper/2109.04119/) (2021)

## Abstract (arXiv)

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).