Investigation of Spiking Neural Networks for Joint Detection and Tracking

Spiking Neural Networks (SNNs), recognized for their dynamic and event-driven capabilities, offer a viable, energy-efficient alternative to conventional Artificial Neural Networks (ANNs), emulating aspects of the human brain’s processing power. This paper provides a comparative study of deterministic SNNs (DSNNs) and probabilistic SNNs (PSNNs), examining their ability to interpret data from event-cameras, which activate only upon significant changes in pixel brightness. By leveraging SNNs, we can directly process sporadic, asynchronous, event-based data, thus fully utilizing the high-temporal resolution, extensive dynamic range, and robustness to motion blur offered by event-cameras. Our investigation aims to deepen the understanding of the operational strengths and weaknesses of these SNN architectures, particularly in detecting and precisely tracking visual events—a critical function for real-time applications such as autonomous vehicle navigation. We created and employed a dataset obtained from a DVXplorer event-camera for this evaluation.