Smartphone region-wise image indoor localization using deep learning for
  indoor tourist attraction
Gabriel Toshio Hirokawa Higa Rodrigo Stuqui Monzani Jorge Fernando da Silva Cecatto Maria Fernanda Balestieri Mariano de Souza Vanessa Aparecida de Moraes Weber Hemerson Pistori Edson Takashi Matsubara
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.