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Open AccessArticle

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

1
ITAP-DISA, University of Valladolid, 47002 Valladolid, Spain
2
Departamento de Ingeniería de Software y Sistemas Informáticos, ETSI Informática, UNED, C/Juan del Rosal, 16, 28040 Madrid, Spain
3
School of Engineering, The University of Edinburgh, King’s Buildings, Edinburgh EH9 3FB, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(24), 5495; https://doi.org/10.3390/s19245495
Received: 8 October 2019 / Revised: 15 November 2019 / Accepted: 29 November 2019 / Published: 12 December 2019
(This article belongs to the Collection Positioning and Navigation)
This work presents an integrated Indoor Positioning System which makes use of WiFi signals and RGB cameras, such as surveillance cameras, to track and identify people navigating in complex indoor environments. Previous works have often been based on WiFi, but accuracy is limited. Other works use computer vision, but the problem of identifying concrete persons relies on such techniques as face recognition, which are not useful if there are many unknown people, or where the robustness decreases when individuals are seen from different points of view. The solution presented in this paper is based on an accurate combination of smartphones along with RGB cameras, such as those used in surveillance infrastructures. WiFi signals from smartphones allow the persons present in the environment to be identified uniquely, while the data coming from the cameras allow the precision of location to be improved. The system is nonintrusive, and biometric data about subjects is not required. In this paper, the proposed method is fully described and experiments performed to test the system are detailed along with the results obtained. View Full-Text
Keywords: indoor positioning; WPS; RGB cameras; WiFi; fingerprint map; trajectory; IPS; computer vision indoor positioning; WPS; RGB cameras; WiFi; fingerprint map; trajectory; IPS; computer vision
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Duque Domingo, J.; Gómez-García-Bermejo, J.; Zalama, E.; Cerrada, C.; Valero, E. Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning. Sensors 2019, 19, 5495.

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