  The main streams of human activity recognition (HAR) algorithms are developed
based on RGB cameras which are suffered from illumination, fast motion,
privacy-preserving, and large energy consumption. Meanwhile, the biologically
inspired event cameras attracted great interest due to their unique features,
such as high dynamic range, dense temporal but sparse spatial resolution, low
latency, low power, etc. As it is a newly arising sensor, even there is no
realistic large-scale dataset for HAR. Considering its great practical value,
in this paper, we propose a large-scale benchmark dataset to bridge this gap,
termed HARDVS, which contains 300 categories and more than 100K event
sequences. We evaluate and report the performance of multiple popular HAR
algorithms, which provide extensive baselines for future works to compare. More
importantly, we propose a novel spatial-temporal feature learning and fusion
framework, termed ESTF, for event stream based human activity recognition. It
first projects the event streams into spatial and temporal embeddings using
StemNet, then, encodes and fuses the dual-view representations using
Transformer networks. Finally, the dual features are concatenated and fed into
a classification head for activity prediction. Extensive experiments on
multiple datasets fully validated the effectiveness of our model. Both the
dataset and source code will be released on
\url{https://github.com/Event-AHU/HARDVS}.
