Computer Vision for Vulnerable Road Users using Machine Learning
- 1 North Carolina A&T State University,, United States
- 2 North Carolina A&T State University, United States
Transportation has become a very important component for all ages in today’s life. However, seniors often find difficulty in travelling by public transport or even by their own vehicles. Thus, they are considered as Vulnerable Road Users (VRUs). This research mainly concentrates on creating an application that helps the elderly mobile users in virtually screening of directions using GPS, tracking of sidewalks, identifying the traffic signals and other sign boards. MATLAB/Simulink serves as the main software used in developing this application. Images and other types of data are captured using sensors on Android devices. Then MATLAB is used to perform deep learning. Transfer learning is another machine learning technique where a model like AlexNet developed for one task is retrained to perform a second task. In this research, AlexNet is used to identify and classify different traffic warning signs in real-time. A detailed description of this method and the results are presented in this paper. Once the MATLAB-based program is developed, it can be converted into Java codes when needed. Using Android Studio, the code can be used in the application. VRUs with mobility impairments and vision deficiencies often find it difficult to use wheelchair on sidewalks. This paper also presents a sidewalk tracking system with a departure warning. In this research, Hough Transform is used to present the detection of sidewalk. This sidewalk tracking system can provide the user with essential information that can minimize the risk of an accident. This system can identify and track the present sidewalks. Any unintended departure towards the edge of sidewalks will be detected and notified to the user. Elaborated implementation of this system and results are presented in this paper. Re-routing the user when they approach the end of the sidewalk is left for future work. The application will further be developed to provide a voice navigation informing the departure warning, a traffic signal or a recognized sign board.
Copyright: © 2019 Devi Chilukuri, Sun Yi and Younho Seong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Computer Vision
- Machine Learning
- Vulnerable Road Users