Event-based Vision for Early Prediction of Manipulation Actions
Daniel Deniz Cornelia Fermuller Eduardo Ros Manuel Rodriguez-Alvarez Francisco Barranco
Neuromorphic visual sensors are artificial retinas that output sequences of
asynchronous events when brightness changes occur in the scene. These sensors
offer many advantages including very high temporal resolution, no motion blur
and smart data compression ideal for real-time processing. In this study, we
introduce an event-based dataset on fine-grained manipulation actions and
perform an experimental study on the use of transformers for action prediction
with events. There is enormous interest in the fields of cognitive robotics and
human-robot interaction on understanding and predicting human actions as early
as possible. Early prediction allows anticipating complex stages for planning,
enabling effective and real-time interaction. Our Transformer network uses
events to predict manipulation actions as they occur, using online inference.
The model succeeds at predicting actions early on, building up confidence over
time and achieving state-of-the-art classification. Moreover, the
attention-based transformer architecture allows us to study the role of the
spatio-temporal patterns selected by the model. Our experiments show that the
Transformer network captures action dynamic features outperforming video-based
approaches and succeeding with scenarios where the differences between actions
lie in very subtle cues. Finally, we release the new event dataset, which is
the first in the literature for manipulation action recognition. Code will be
available at https://github.com/DaniDeniz/EventVisionTransformer.