  The mainstream human activity recognition (HAR) algorithms are developed
based on RGB cameras, which are easily influenced by low-quality images (e.g.,
low illumination, motion blur). Meanwhile, the privacy protection issue caused
by ultra-high definition (HD) RGB cameras aroused more and more people's
attention. Inspired by the success of event cameras which perform better on
high dynamic range, no motion blur, and low energy consumption, we propose to
recognize human actions based on the event stream. We propose a lightweight
uncertainty-aware information propagation based Mobile-Former network for
efficient pattern recognition, which aggregates the MobileNet and Transformer
network effectively. Specifically, we first embed the event images using a stem
network into feature representations, then, feed them into uncertainty-aware
Mobile-Former blocks for local and global feature learning and fusion. Finally,
the features from MobileNet and Transformer branches are concatenated for
pattern recognition. Extensive experiments on multiple event-based recognition
datasets fully validated the effectiveness of our model. The source code of
this work will be released at
https://github.com/Event-AHU/Uncertainty_aware_MobileFormer.
