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    ARTICLE

    An Efficient Indoor Localization Based on Deep Attention Learning Model

    Amr Abozeid1,*, Ahmed I. Taloba1,2, Rasha M. Abd El-Aziz1,3, Alhanoof Faiz Alwaghid1, Mostafa Salem3, Ahmed Elhadad1,4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2637-2650, 2023, DOI:10.32604/csse.2023.037761

    Abstract Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, image-based localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization’s accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This… More >

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