Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909222409601024 |
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| author | Higa, Gabriel Toshio Hirokawa Monzani, Rodrigo Stuqui Cecatto, Jorge Fernando da Silva de Souza, Maria Fernanda Balestieri Mariano Weber, Vanessa Aparecida de Moraes Pistori, Hemerson Matsubara, Edson Takashi |
| author_facet | Higa, Gabriel Toshio Hirokawa Monzani, Rodrigo Stuqui Cecatto, Jorge Fernando da Silva de Souza, Maria Fernanda Balestieri Mariano Weber, Vanessa Aparecida de Moraes Pistori, Hemerson Matsubara, Edson Takashi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction Higa, Gabriel Toshio Hirokawa Monzani, Rodrigo Stuqui Cecatto, Jorge Fernando da Silva de Souza, Maria Fernanda Balestieri Mariano Weber, Vanessa Aparecida de Moraes Pistori, Hemerson Matsubara, Edson Takashi Computer Vision and Pattern Recognition 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. |
| title | Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.07621 |