Two-Stream Spiking Neural Network for Event-based Action Recognition

Abstract

Spiking neural networks (SNNs) are increasingly applied to event-based data generated by event cameras due to their asynchronous and sparse properties. Event cameras can inherently respond to the changes in the scene, which is a quite desirable property for action recognition tasks. However, existing works of SNNs for event-based action recognition are still limited. To capture the rich dynamics embedded in event streams, we propose the two-stream SNN that consists of spatial spiking stream and motion spiking stream to address event-based action recognition. To effectively build the two-stream SNN, we present a motion feature aggregation strategy and an attention-based two-stream fusion method. The motion feature aggregation strategy accumulates motion information and groups it into distinct channels for input into the SNN, which can alleviate the dilemma of information loss caused by compact representation. The attention-based two-stream fusion method can fuse the spatial and motion features effectively using the channel-wise attention mechanism, which helps our network to achieve better integration of two-stream information. Extensive experimental results on three event-based action recognition datasets show our proposed two-stream SNN achieves competitive performance with much fewer trainable parameters, which demonstrates the effectiveness of our work in event-based action recognition tasks.