A Novel Spike Vision Approach for Robust Multi-Object Detection Using SNNs

EasyChair Preprint 11921

Abstract

In this paper, we propose a novel system that combines computer vision techniques with SNNs to detect spike vision-based multi-object tracking. Our system integrates computer vision techniques for robust and accurate detection and tracking, extracts regions of interest (ROIs) for focused analysis, and simulates spiking neurons for biologically inspired representation. Our approach advances the understanding of visual processing and empowers the development of efficient SNN models. In addition, our approach has achieved state-of-the-art results in visual processing tasks, showcasing the effectiveness and superiority of our approach. Extensive experiments and evaluations have been conducted to demonstrate the effectiveness and superiority of our proposed architecture and algorithm. The results obtained from our system are provided in this paper, showcasing the revolutionary performance that validates the efficacy of our approach and establishes it as a promising solution in the field of SNNs.

Keyphrases: Spiking Neural Network, multi-object detection, neural network models

@booklet{EasyChair:11921,
  author    = {Sanaullah and Shamini Koravuna and Ulrich Rückert and Thorsten Jungeblut},
  title     = {A Novel Spike Vision Approach for Robust Multi-Object Detection Using SNNs},
  howpublished = {EasyChair Preprint 11921},
  year      = {EasyChair, 2024}}