Open AccessTraffic light and moving object detection for a guide‐dog robot
Author(s) -
Chen Qiang,
Chen Yig,
Zhu Jinhui,
De Luca Gennaro,
Zhang Mei,
Guo Ying
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1137
Subject(s) - computer science , intersection (aeronautics) , computer vision , robot , artificial intelligence , process (computing) , object detection , object (grammar) , human–computer interaction , real time computing , pattern recognition (psychology) , engineering , aerospace engineering , operating system
Guide dogs are helpful for visually impaired people for navigating through the streets. However, it is expensive and time consuming to train a guide dog. In addition, a guide dog cannot decide when and where to cross a street safely, and it is up to the human to decide. Here, the authors propose a framework for creating a guide dog robot by using artificial intelligence and other technologies. The proposed framework is based on an Intel UP squared board, together with a Neural Compute Stick Movidius to process the images gathered from a GoPro camera. MobileNet single shot detector (SSD) is the main framework to detect the moving objects in the environment. The final decision is made after fusing the information gathered from all the sources. The authors also apply the Amazon Alexa device for the voice communication between the guide dog robot and the visually impaired person. A prototype of the proposed system is implemented and tested. Experimental results show that the proposed framework can process the information at a traffic intersection scene and can guide a blind person to cross the street safely.