Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings

Research Article

Indoor Visual Positioning Based on Image Retrieval in Dense Connected Convolutional Network

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  • @INPROCEEDINGS{10.1007/978-3-030-69066-3_7,
        author={Xiaomeng Guo and Danyang Qin and Yan Yang},
        title={Indoor Visual Positioning Based on Image Retrieval in Dense Connected Convolutional Network},
        proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings},
        proceedings_a={AICON},
        year={2021},
        month={7},
        keywords={DenseNet Feature extraction Image retrieval Indoor positioning},
        doi={10.1007/978-3-030-69066-3_7}
    }
    
  • Xiaomeng Guo
    Danyang Qin
    Yan Yang
    Year: 2021
    Indoor Visual Positioning Based on Image Retrieval in Dense Connected Convolutional Network
    AICON
    Springer
    DOI: 10.1007/978-3-030-69066-3_7
Xiaomeng Guo1, Danyang Qin1, Yan Yang1
  • 1: Heilongjiang University

Abstract

As now available methods or systems based on image retrieval and visual researchs are implemented in an indoor environment, their retrieval accuracy and real-time positioning still have their own limitations. For this reason, this paper designs a visual indoor positioning system based on densely connected convolutional network image retrieval. Combine visual positioning with DenseNet-based image retrieval method. The problem of excessively deep network layers caused by the original convolutional neural network in pursuit of high retrieval accuracy is improved. Under the advantage of ensuring the high accuracy of image retrieval based on depth features, the problem of low real-time positioning caused by the long training time of the convolutional network model is improved. The simulation results show the feasibility of the positioning method in indoor environment, and the comparison experiment verifies the improvement of accuracy and speed as well as the reliability of the method.