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How to navigate safely, recognize encountered obstacles, and move independently from one location to another in unknown environments are some of the challenges that face visually impaired people. By proposing a solution towards overcoming these challenges, this work will be of most importance to visually impaired people. In this work, we propose a consistent, reliable and robust smartphone-based method to classify obstacles in unknown environments from partial visual information based on computer vision and machine learning techniques. Our proposed method handles high levels of noise and bad resolution in frames captured from a phone camera. In addition, our proposed method offers maximum flexibility to users and use the least expensive equipment possible. Moreover, by leveraging on deep-learning techniques, the proposed method enables semantic categorization in order to classify obstacles and increase the awareness of the explored environment. The efficiency of the work has been experimentally measured on a variety of experiments studies on different complex scenes. It records high accuracy of [90.2 %].

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Deep Multi-Layer Perceptron-Based Obstacle Classification Method From Partial Visual Information: Application to the Assistance of Visually Impaired People