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Event cameras are neuromorphic sensors that capture asynchronous and sparseevent stream when per-pixel brightness changes. The state-of-the-art processingmethods for event signals typically aggregate events into a frame or a grid.However, events are dense in time, these works are limited to local informationof events due to the stacking. In this paper, we present a novel spatiotemporalrepresentation learning method which can capture the global correlations of allevents in the event stream simultaneously by tensor decomposition. In addition,with the events are sparse in space, we propose an Elastic Net-incorporatedtensor network (ENTN) model to obtain more spatial and temporal details aboutevent stream. Empirically, the results indicate that our method can representthe spatiotemporal correlation of events with high quality, and can achieveeffective results in applications like filtering noise compared with thestate-of-the-art methods.

Representation Learning on Event Stream via an Elastic Net-incorporated Tensor Network