Ubiquitous WLAN/Camera Positioning using Inverse Intensity Chromaticity Space-based Feature Detection and Matching: A Preliminary Result

TL;DR

This paper introduces a novel feature detection and matching algorithm that combines WLAN signal strength and camera data to localize users indoors, aiming to reduce computational complexity and improve positioning accuracy.

Contribution

It presents a new intensity chromaticity space-based method that fuses wireless and visual data for indoor positioning without conventional search algorithms.

Findings

Preliminary results show promising localization accuracy.

The method reduces computational complexity compared to traditional approaches.

Indoor environment tests validate the system's potential effectiveness.

Abstract

This paper present our new intensity chromaticity space-based feature detection and matching algorithm. This approach utilizes hybridization of wireless local area network and camera internal sensor which to receive signal strength from a access point and the same time retrieve interest point information from hallways. This information is combined by model fitting approach in order to find the absolute of user target position. No conventional searching algorithm is required, thus it is expected reducing the computational complexity. Finally we present pre-experimental results to illustrate the performance of the localization system for an indoor environment set-up.