Title :
Semi-supervised logo-based indoor localization using smartphone cameras
Author :
Sadeghi, Hamed ; Valaee, Shahrokh ; Shirani, Shahram
Author_Institution :
ECE Dept., Univ. of Toronto, Toronto, ON, Canada
Abstract :
In this paper, we propose a homography-aware semi-supervised formulation for the logo-based indoor localization problem using smartphone cameras. Our method labels unmatched feature points detected inside the logo parts of query images with their estimated 3D coordinates. The 3D coordinates are computed using the homography estimated from the matched features. We demonstrate the accuracy improvement and lower localization error variance resulted from our semi-supervised approach via experiments in an indoor scenario.
Keywords :
feature extraction; image matching; image retrieval; learning (artificial intelligence); smart phones; 3D coordinates; homography-aware semi-supervised formulation; localization error variance; query images; semi-supervised logo-based indoor localization problem; smartphone cameras; unmatched feature points detection; Accuracy; Cameras; Databases; Estimation; Feature extraction; Three-dimensional displays; Transmission line matrix methods; Coplanar feature points; Indoor localization; Logo; SURF feature points; Semi-supervised learning;
Conference_Titel :
Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014 IEEE 25th Annual International Symposium on
DOI :
10.1109/PIMRC.2014.7136504
