A Compressive Sensing Approach to Describe Indoor Scenes for Blind People

This paper introduces a new portable camera-based method for helping blind people to recognize indoor objects. Unlike state-of-the-art techniques, which typically perform the recognition task by limiting it to a single predefined class of objects, we propose here a completely different alternative scheme, defined as coarse description. It aims at expanding the recognition task to multiple objects and, at the same time, keeping the processing time under control by sacrificing some information details. The benefit is to increment the awareness and the perception of a blind person to his direct contextual environment. The coarse description issue is addressed via two image multilabeling strategies which differ in the way image similarity is computed. The first one makes use of the Euclidean distance measure, while the second one relies on a semantic similarity measure modeled by means of Gaussian process estimation. To achieve fast computation capability, both strategies rely on a compact image representation based on compressive sensing. The proposed methodology was assessed on two indoor datasets representing different indoor environments. Encouraging results were achieved in terms of both accuracy and processing time.