Deep combining of local phase quantization and histogram of oriented gradients for indoor positioning based on smartphone camera
To achieve high accuracy in indoor positioning using a smartphone, there are two limitations: (1) limited computational and memory resources of the smartphone and (2) the human walking in large buildings. To address these issues, we propose a new feature descriptor by deeply combining histogram of o...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716686978 |
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| Summary: | To achieve high accuracy in indoor positioning using a smartphone, there are two limitations: (1) limited computational and memory resources of the smartphone and (2) the human walking in large buildings. To address these issues, we propose a new feature descriptor by deeply combining histogram of oriented gradients and local phase quantization. This feature is a local phase quantization of a salient histogram of oriented gradient visualizing image, which is robust in indoor scenarios. Moreover, we introduce a base station–based indoor positioning system for assisting to reduce the image matching at runtime. The experimental results show that accurate and efficient indoor location positioning is achieved. |
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| ISSN: | 1550-1477 |