Guidance system for Visually Impaired Persons using Deep Learning and
  Optical flow
Shwetang Dubey Alok Ranjan Sahoo Pavan Chakraborty
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.