Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6937478 | Computer Vision and Image Understanding | 2017 | 32 Pages |
Abstract
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 inefficient. The recognition results on a specifically recorded corpus of 151 videos with simple geometrical objects show the mean Average Precision (mAP) of 64,6% and the computational time at the generalization lower than a time of a visual fixation on the object of interest.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Philippe Pérez de San Roman, Jenny Benois-Pineau, Jean-Philippe Domenger, Florent Paclet, Daniel Cataert, Aymar de Rugy,