Guidance system for Visually Impaired Persons using Deep Learning and Optical flow
This addresses the challenge for visually impaired individuals to navigate dynamic environments, though it is incremental as it integrates existing methods.
The paper tackles the problem of guiding visually impaired persons in busy streets by detecting approaching objects and their direction using a combination of YOLOv3 for object detection, Lucas Kanade's optical flow for motion estimation, and Depth-net for depth estimation, with real-world testing demonstrating effectiveness.
Visually impaired persons find it difficult to know about their surroundings while walking on a road. Walking sticks used by them can only give them information about the obstacles in the stick's proximity. Moreover, it is mostly effective in static or very slow-paced environments. Hence, this paper introduces a method to guide them in a busy street. To create such a system it is very important to know about the approaching object and its direction of approach. To achieve this objective we created a method in which the image frame received from the video is divided into three parts i.e. center, left, and right to know the direction of approach of the approaching object. Object detection is done using YOLOv3. Lucas Kanade's optical flow estimation method is used for the optical flow estimation and Depth-net is used for depth estimation. Using the depth information, object motion trajectory, and object category information, the model provides necessary information/warning to the person. This model has been tested in the real world to show its effectiveness.