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Moya-Albor, Ernesto
Stair Climbing Robot Based on Convolutional Neural Networks for Visual Impaired
Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras
2019
,
Campos, Guillermo
,
Poza, David
,
Reyes, Moises
,
Zacate, Alma
,
Ponce, Hiram
,
Brieva, Jorge
,
Moya-Albor, Ernesto
When a person loses the sense of sight, in general, it is suggested to use a white cane to perform daily activities. However, using a white cane limits the movement of a person. In addition, guide dogs can be served in this impairment. However, the acquisition and maintenance of a guide dog is extremely high for people in development countries. In this regard, this paper presents a proof-of-concept of a low-cost robotic system able to guide a visual impaired, as a guide dog. The robot is specially designed for climbing stairs at indoors, and it uses convolutional neural networks (CNN) for both object detection and hand gesture recognition for special instructions from the user. Experimental results showed that our prototype robot can climb stairs with 86.7% of efficiency in concrete stair surfaces. Also, the visual representation by CNN performed more than 98% accuracy. © 2019 IEEE.
2020
,
Espinosa Loera, Ricardo Abel
,
Ponce, Hiram
,
Gutiérrez, Sebastián
,
Martinez-Villaseñor, Lourdes
,
Moya-Albor, Ernesto
,
Brieva, Jorge
Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.