# Computational models of object motion detectors accelerated using FPGA technology

arXiv: https://arxiv.org/abs/2310.06842 · HTML: https://mlsanity.com/paper/2310.06842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 316 references that are in the map.
- First posted on arXiv 23 Aug 2023.

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

- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning](https://mlsanity.com/paper/1611.03000/) (2016)
- [Transferring Rich Feature Hierarchies for Robust Visual Tracking](https://mlsanity.com/paper/1501.04587/) (2015)
- [A Deep Convolutional Neural Network for Background Subtraction](https://mlsanity.com/paper/1702.01731/) (2017)
- [Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularization](https://mlsanity.com/paper/1901.10991/) (2019)
- [Background subtraction using the factored 3-way restricted Boltzmann machines](https://mlsanity.com/paper/1802.01522/) (2018)

## Abstract (arXiv)

This PhD research introduces three key contributions in the domain of object motion detection: Multi-Hierarchical Spiking Neural Network (MHSNN): A specialized four-layer Spiking Neural Network (SNN) architecture inspired by vertebrate retinas. Trained on custom lab-generated images, it exhibited 6.75% detection error for horizontal and vertical movements. While non-scalable, MHSNN laid the foundation for further advancements. Hybrid Sensitive Motion Detector (HSMD): Enhancing Dynamic Background Subtraction (DBS) using a tailored three-layer SNN, stabilizing foreground data to enhance object motion detection. Evaluated on standard datasets, HSMD outperformed OpenCV-based methods, excelling in four categories across eight metrics. It maintained real-time processing (13.82-13.92 fps) on a high-performance computer but showed room for hardware optimisation. Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD): Building upon HSMD, this adaptation implemented the SNN component on dedicated hardware (FPGA). OpenCL simplified FPGA design and enabled portability. NeuroHSMD demonstrated an 82% speedup over HSMD, achieving 28.06-28.71 fps on CDnet2012 and CDnet2014 datasets. These contributions collectively represent significant advancements in object motion detection, from a biologically inspired neural network design to an optimized hardware implementation that outperforms existing methods in accuracy and processing speed.