Object Identification and Safe Route Recommendation Based on Human Flow for the Visually Impaired

Abstract

It is difficult for visually impaired people to move indoors and outdoors. In 2018, world health organization (WHO) reported that there were about 253 million people around the world who were moderately visually impaired in distance vision. A navigation system that combines positioning and obstacle detection has been actively researched and developed. However, when these obstacle detection methods are used in high-traffic passages, since many pedestrians cause an occlusion problem that obstructs the shape and color of obstacles, these obstacle detection methods significantly decrease in accuracy. To solve this problem, we developed an application “Follow me!”. The application recommends a safe route by machine learning the gait and walking route of many pedestrians obtained from the monocular camera images of a smartphone. As a result of the experiment, pedestrians walking in the same direction as visually impaired people, oncoming pedestrians, and steps were identified with an average accuracy of 0.92 based on the gait and walking route of pedestrians acquired from monocular camera images. Furthermore, the results of the recommended safe route based on the identification results showed that the visually impaired people were guided to a safe route with 100% accuracy. In addition, visually impaired people avoided obstacles that had to be detoured during construction and signage by walking along the recommended route.

Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment

“…Authors in 38 stated that Safe route recommendation is the significant process in visually impaired people navigation. In general, visually impaired peoples suffer a lot in the presence of heavy vehicle or human flow.…”

Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map

“…The Land Use Regression (LUR) model was used to model the concentration of black carbon to be calculated in the route candidates for home-to-school commuting [11]. Other researches have aimed at designing route recommendation algorithms for the specific benefit of Chronic Obstructive Pulmonary Disease (COPD) patients [15], visually impaired users [16], and Patients with Motor Disabilities (PWMD) [17]. Such researches have given more importance to the customization of the algorithm using user-specific necessities.…”

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

“…Bai et al [ 23 ] mounted a RGB-D sensor on a pair of eyeglasses and designed a lightweight convolutional neural network (CNN)-based 2.5D object recognition module for deployment on a smartphone, providing obstacle category, location and orientation information. Kajiwara and Kimura [ 24 ] designed an object identification and route recommendation system based on human flow for the visually impaired. Specifically, they used the OpenPose model [ 25 ] to detect human skeletons using a RGB-D camera, where the depth maps enabled the localization of the pedestrian’s skeleton trunks for human flow avoidance.…”