Computational models of object motion detectors accelerated using FPGA technology

Abstract: 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.