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| author_facet |
Guo, Wei Wu, Ran Chen, Yanhua Zhu, Xinyan Guo, Wei Wu, Ran Chen, Yanhua Zhu, Xinyan |
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Guo, Wei Wu, Ran Chen, Yanhua Zhu, Xinyan |
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Guo, Wei Wu, Ran Chen, Yanhua Zhu, Xinyan Sensors Deep Learning Scene Recognition Method Based on Localization Enhancement Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry |
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guo, wei |
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Guo, Wei Wu, Ran Chen, Yanhua Zhu, Xinyan 1424-8220 MDPI AG Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry http://dx.doi.org/10.3390/s18103376 <jats:p>With the rapid development of indoor localization in recent years; signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved; in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.</jats:p> Deep Learning Scene Recognition Method Based on Localization Enhancement Sensors |
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| title |
Deep Learning Scene Recognition Method Based on Localization Enhancement |
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Deep Learning Scene Recognition Method Based on Localization Enhancement |
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Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_fullStr |
Deep Learning Scene Recognition Method Based on Localization Enhancement |
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Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_short |
Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_sort |
deep learning scene recognition method based on localization enhancement |
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Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry |
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http://dx.doi.org/10.3390/s18103376 |
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2018 |
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<jats:p>With the rapid development of indoor localization in recent years; signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved; in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.</jats:p> |
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| author | Guo, Wei, Wu, Ran, Chen, Yanhua, Zhu, Xinyan |
| author_facet | Guo, Wei, Wu, Ran, Chen, Yanhua, Zhu, Xinyan, Guo, Wei, Wu, Ran, Chen, Yanhua, Zhu, Xinyan |
| author_sort | guo, wei |
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| description | <jats:p>With the rapid development of indoor localization in recent years; signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved; in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.</jats:p> |
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| spelling | Guo, Wei Wu, Ran Chen, Yanhua Zhu, Xinyan 1424-8220 MDPI AG Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry http://dx.doi.org/10.3390/s18103376 <jats:p>With the rapid development of indoor localization in recent years; signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved; in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.</jats:p> Deep Learning Scene Recognition Method Based on Localization Enhancement Sensors |
| spellingShingle | Guo, Wei, Wu, Ran, Chen, Yanhua, Zhu, Xinyan, Sensors, Deep Learning Scene Recognition Method Based on Localization Enhancement, Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry |
| title | Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_full | Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_fullStr | Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_full_unstemmed | Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_short | Deep Learning Scene Recognition Method Based on Localization Enhancement |
| title_sort | deep learning scene recognition method based on localization enhancement |
| title_unstemmed | Deep Learning Scene Recognition Method Based on Localization Enhancement |
| topic | Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry |
| url | http://dx.doi.org/10.3390/s18103376 |