Neuromorphic Motion Segmentation with Spiking Neural Networks for Robotic Perception

Event-based cameras have emerged as a promising technology for robotic applications, offering high temporal resolution and low latency. Motion segmentation is a critical task for dynamic environments, enabling essential capabilities such as obstacle avoidance, SLAM, and autonomous navigation. In this work, we propose a spiking neural network (SNN) architecture based on a U-Net encoder-decoder to perform motion segmentation using event streams captured by event-based cameras. The network processes spatio-temporal data efficiently and is trained with a combination of Dice loss and binary cross-entropy to handle imbalanced segmentation masks. We leverage the EVIMO dataset, which provides pixel-wise motion labels, egomotion data, and event streams, to train and evaluate the model. Our method achieves an Intersection over Union (IoU) score of 51 . 6%, comparable to state-of-the-art approaches like SpikeMS, while maintaining computational efficiency. Qualitative results show accurate pixel-wise segmentation of dynamic objects, demonstrating clear visual alignment between the predictions and the ground truth. These results highlight the potential of our approach for deployment in neu-romorphic robotic systems requiring real-time processing.