Zainab Mohammed Resan; Muayad Sadik Croock
Abstract
Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, ...
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Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, if it wanted to raise the precision ofindoor classification, the number of image dataset of the indoor environment should be aslarge as possible to satisfy and cover the underlying area. In this work, a smartphonecamera is used to build the image-based dataset of the investigated building. In addition,captured images in real time are taken to be processed with the proposed model as a testset. The proposed indoor localization includes two phases the first one is the offlinelearning phase and the second phase is the online processing phase. In the offline learningphase, here we propose a convolutional neural network (CNN) model that sequences thefeatures of image data from some classis's dataset composed with a smartphone camera.In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appointthe expected indoor location. The proposed system has been tested over variousexperiments and the obtained experimental results show that the accuracy of the predictionis almost 98.0%.
Noor Abdul Khaleq Zghair; Muayad Sadik Croock; Ali Abdul Razzaq Taresh
Volume 19, Issue 2 , April 2019, , Page 69-77
Abstract
Recently, indoor localization has witnessed an increase in interest,due to the potential wide range of using in different applications, such asInternet of Things (IoT). It is also providing a solution for the absence of GlobalPositioning System (GPS) signals inside buildings. Different techniques havebeen ...
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Recently, indoor localization has witnessed an increase in interest,due to the potential wide range of using in different applications, such asInternet of Things (IoT). It is also providing a solution for the absence of GlobalPositioning System (GPS) signals inside buildings. Different techniques havebeen used for performing the indoor localization, such as sensors and wirelesstechnologies. In this paper, an indoor localization and object tracking system isproposed based on WiFi transmission technique. It is done by distributingdifferent WiFi sources around the building to read the data of the trackedobjects. This is to measure the distance between the WiFi receiver and theobject to allocate and track it efficiently. The test results show that the proposedsystem is working in an efficient way with low cost.
