Translation and Scale Invariance for Event-Based Object tracking

Without temporal averaging, such as rate codes, it remains challenging to train spiking neural networks for temporal regression tasks. In this work, we present a novel method to accurately predict spatial coordinates from event data with a fully spiking convolutional neural network (SCNN) without temporal averaging. Our method performs on-par with artificial neural networks (ANN) of similar complexity. Additionally, we demonstrate faster convergence in half the time using translation- and scale-invariant receptive fields. To permit comparison with conventional frame-based ANNs, we base our results on a simulated event-based dataset with an unrealistic high density. Therefore, we hypothesize that our method significantly outperform ANNs in settings with lower event density, as seen in real-life event-based data. Our model is fully spiking and can be ported directly to neuromorphic hardware.