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

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... fic 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.