Att Sammanföra Eventkameror och Spikande Neurala Nätverk för Smarta Trafikobservationssystem

Spiking neural networks (SNNs) are a kind of neural networks capable of processing event-based data in real-time, resource-constrained environments. This study focuses on evaluating the performance of SNNs for object tracking, specifically tracking cars. The primary goal of this research is to investigate and compare different SNN architectures in the context of traffic monitoring, then adjust the number of parameters on the best-performing architecture to investigate differences in performance. The results are analyzed to understand further how different SNN designs perform, and the implications of these findings are discussed. The network demonstrated the ability to learn key features of cars and disregard irrelevant events, validating the viability of using a spiking neural network for traffic tracking. The thesis highlights the importance of optimizing the trade-off between network size and performance, particularly when working with resource-constraint applications.