  The recognition of human actions in videos is one of the most active research
fields in computer vision. The canonical approach consists in a more or less
complex preprocessing stages of the raw video data, followed by a relatively
simple classification algorithm. Here we address recognition of human actions
using the reservoir computing algorithm, which allows us to focus on the
classifier stage. We introduce a new training method for the reservoir
computer, based on "Timesteps Of Interest", which combines in a simple way
short and long time scales. We study the performance of this algorithm using
both numerical simulations and a photonic implementation based on a single
non-linear node and a delay line on the well known KTH dataset. We solve the
task with high accuracy and speed, to the point of allowing for processing
multiple video streams in real time. The present work is thus an important step
towards developing efficient dedicated hardware for video processing.
