  The recognition of human actions in video streams is a challenging task in
computer vision, with cardinal applications in e.g. brain-computer interface
and surveillance. Deep learning has shown remarkable results recently, but can
be found hard to use in practice, as its training requires large datasets and
special purpose, energy-consuming hardware. In this work, we propose a scalable
photonic neuro-inspired architecture based on the reservoir computing paradigm,
capable of recognising video-based human actions with state-of-the-art
accuracy. Our experimental optical setup comprises off-the-shelf components,
and implements a large parallel recurrent neural network that is easy to train
and can be scaled up to hundreds of thousands of nodes. This work paves the way
towards simply reconfigurable and energy-efficient photonic information
processing systems for real-time video processing.
