  Event camera is an asynchronous, high frequency vision sensor with low power
consumption, which is suitable for human action understanding task. It is vital
to encode the spatial-temporal information of event data properly and use
standard computer vision tool to learn from the data. In this work, we propose
a timestamp image encoding 2D network, which takes the encoded spatial-temporal
images with polarity information of the event data as input and output the
action label. In addition, we propose a future timestamp image generator to
generate futureaction information to aid the model to anticipate the human
action when the action is not completed. Experiment results show that our
method can achieve the same level of performance as those RGB-based benchmarks
on real world action recognition,and also achieve the state of the art (SOTA)
result on gesture recognition. Our future timestamp image generating model can
effectively improve the prediction accuracy when the action is not completed.
We also provide insight discussion on the importance of motion and appearance
information in action recognition and anticipation.
