Indoor Localization using Region-based Convolutional Neural Network

2017 ◽  
Author(s):  
Haowei Xu ◽  
Zoltan Koppanyi ◽  
Charles K. Toth ◽  
Dorota Brzezinska
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Indoor Localization

scholarly journals SurfCNN: A Descriptor Accelerated Convolutional Neural Network for Image-Based Indoor Localization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 59750-59759
Author(s):  
Ahmed M. Elmoogy ◽  
Xiaodai Dong ◽  
Tao Lu ◽  
Robert Westendorp ◽  
Kishore Reddy Tarimala
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Indoor Localization

scholarly journals Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 621 ◽  
Author(s):  
Baoding Zhou ◽  
Jun Yang ◽  
Qingquan Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Indoor Environment ◽  
Semantic Information ◽  
Indoor Localization ◽  
Academic Research ◽  
Recognition Method

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.

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