  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.
