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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
