Collision risk prediction for visually impaired people using high level information fusion. (May 2019) - Cordeiro, Natal Henrique; Pedrino, Emerson Carlos
Abstract: The technologies developed so far to help visually impaired people (VIP) navigate meet only some of their everyday needs. This project allows the visually impaired to improve the comprehension of their context by generating a risk map following an analysis of the position, distance, size and motion of the objects present in their environment. This comprehension is refined by data fusion steps applied to the High Level Information Fusion (HLIF) to predict possible impacts in the near future. A risk map is made up of probabilities generated after executing a set of inferences. These inferences allow the evaluation of future collision risks in different directions by detecting static objects, detecting free passage and analyzing paths followed by dynamic objects in a 3D plane. Different datasets were modeled and a comparative analysis was performed to check the percentage of correct answers and the accuracy of the inferences made using different classifiers. Thus, in order to demonstrate the advantages of the HLIF implementation in a dedicated VIP navigation system, the proposed architecture was tested against three other navigation systems that use different approaches. The generation of specific results made it possible to validate and compare these navigation systems. For this comparative analysis, different environments were used with the goal of indicating a direction for the VIP to move in with fewer collision risks. In addition to providing a risk map giving possible collisions, this project system provided greater reliability for navigation, especially when obstacles were very close and moving objects were detected and tracked. Highlights: A novel architecture for Collision Risk Prediction for Visually Impaired People. The system indicates a direction for the VIP to move in with fewer collision risks. The Collision Risk Prediction was performed using High Level Information Fusion. A risk map is made up of probabilities generated after executing a set of inferences. Different dataset models for Collision Risk Prediction were produced.