Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction
This addresses the problem of high infrastructure costs for indoor localization in museums and aquariums, offering a low-cost alternative, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled indoor localization in tourist attractions by using deep learning to classify locations from smartphone images, achieving around 90% precision and 89% recall on a new dataset of 3654 images from a real-world scenario in Brazil.
Smart indoor tourist attractions, such as smart museums and aquariums, usually require a significant investment in indoor localization devices. The smartphone Global Positional Systems use is unsuitable for scenarios where dense materials such as concrete and metal block weaken the GPS signals, which is the most common scenario in an indoor tourist attraction. Deep learning makes it possible to perform region-wise indoor localization using smartphone images. This approach does not require any investment in infrastructure, reducing the cost and time to turn museums and aquariums into smart museums or smart aquariums. This paper proposes using deep learning algorithms to classify locations using smartphone camera images for indoor tourism attractions. We evaluate our proposal in a real-world scenario in Brazil. We extensively collect images from ten different smartphones to classify biome-themed fish tanks inside the Pantanal Biopark, creating a new dataset of 3654 images. We tested seven state-of-the-art neural networks, three being transformer-based, achieving precision around 90% on average and recall and f-score around 89% on average. The results indicate good feasibility of the proposal in a most indoor tourist attractions.