Indoor Place Recognition and Localization Using Histogram of Oriented Gradient with Deep Learning

Zina Khaleel Jalal; Moayad Y. Potrus; Abbas M. Ali

doi:10.21271/ZJPAS.32.1.3

Authors

Zina Khaleel Jalal Department of Software and Informatics Engineering,College of Engineering, Salahaddin University-Erbil,Kurdistan Region, Iraq
Moayad Y. Potrus Department of Software and Informatics Engineering,College of Engineering, Salahaddin University-Erbil,Kurdistan Region, Iraq
Abbas M. Ali Department of Software and Informatics Engineering,College of Engineering, Salahaddin University-Erbil,Kurdistan Region, Iraq

DOI:

https://doi.org/10.21271/ZJPAS.32.1.3

Keywords:

Place recognition, Localization, CNN, AlexNet, SIFT, HOG

Abstract

Indoor place recognition is a crucial and challenging field of computer science. It is widely used in robotics and computer vision for various applications. The challenges in indoor place recognition comes from the fact that recognizing localized places like office, corridor, and others may fall under various environmental effects of weather, illumination and others. In this paper, an indoor place recognition and localization system is proposed. The system utilizes the great recognition capabilities of Convolutional Neural Network (CNN) and AlexNet with the use of feature image for training. The feature images are constructed using Histogram of Gradient (HOG). The main contribution of this work is the use of 2D feature constructed image from HOG instead of the scene image used with CNN. The proposed system was compared to other previous systems, in which, it achieved better recognition accuracy when tested on COLD and IDOL standard indoor image datasets.

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