Computational models of object motion detectors accelerated using FPGA technology

• URL: http://arxiv.org/abs/2310.06842v1
• Date: Wed, 23 Aug 2023 20:26:12 GMT
• Title: Computational models of object motion detectors accelerated using FPGA technology
• Authors: Pedro Machado
• Abstract summary: This PhD research introduces three key contributions in the domain of object motion detection. MHSNN: A specialized four-layer Spiking Neural Network architecture inspired by vertebrate retinas. HSMD: Enhancing Dynamic Background Subtraction (DBS) using a tailored three-layer SNN. NeuroHSMD: Building upon HSMD, this adaptation implemented the SNN component on dedicated hardware.
• Score: 0.3626013617212667
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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