An indoor positioning system facilitated by computer vision

2016 ◽  
Author(s):  
Avi Cooper ◽  
Poojit Hegde
Keyword(s):  
Computer Vision ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Positioning System

scholarly journals A Low Cost Indoor Positioning System Using Computer Vision

2019 ◽  
Vol 11 (4) ◽  
pp. 8-25 ◽  
Author(s):  
Youssef N. Naggar ◽  
◽  
Ayman H. Kassem ◽  
Mohamed S. Bayoumi
Keyword(s):  
Computer Vision ◽  
Low Cost ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Positioning System

scholarly journals Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5495
Author(s):  
Jaime Duque Domingo ◽  
Jaime Gómez-García-Bermejo ◽  
Eduardo Zalama ◽  
Carlos Cerrada ◽  
Enrique Valero
Keyword(s):  
Computer Vision ◽  
Wireless Networks ◽  
Face Recognition ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Environments ◽  
Biometric Data ◽  
Surveillance Cameras ◽  
Points Of View ◽  
Indoor Positioning System

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.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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