  The problem of object recognition in natural scenes has been recently
successfully addressed with Deep Convolutional Neuronal Networks giving a
significant break-through in recognition scores. The computational efficiency
of Deep CNNs as a function of their depth, allows for their use in real-time
applications. One of the key issues here is to reduce the number of windows
selected from images to be submitted to a Deep CNN. This is usually solved by
preliminary segmentation and selection of specific windows, having outstanding
"objectiveness" or other value of indicators of possible location of objects.
In this paper we propose a Deep CNN approach and the general framework for
recognition of objects in a real-time scenario and in an egocentric
perspective. Here the window of interest is built on the basis of visual
attention map computed over gaze fixations measured by a glass-worn
eye-tracker. The application of this set-up is an interactive user-friendly
environment for upper-limb amputees. Vision has to help the subject to control
his worn neuro-prosthesis in case of a small amount of remaining muscles when
the EMG control becomes unefficient. The recognition results on a specifically
recorded corpus of 151 videos with simple geometrical objects show the mAP of
64,6\% and the computational time at the generalization lower than a time of a
visual fixation on the object-of-interest.
