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Cross platform web-based smart tourism using deep monument mining

Etaati, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/PRIA.2019.8785975
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Tourism is one of the largest sources of economic revenue for many countries around the world. The historical and cultural treasures of Iran made it one the main destinations for international tourists. One of the biggest problems encountered by the tourists during the visit to monuments of Iran is the lack of information about the visited landmark. Given that cameras can be found in all of the smart phones, the use of the landmark's photos can be very important for obtaining information about the tourism sites. The detection of the landmarks in an image taken by the mobile phone camera can be a very complex task depending on the angle and the light situation in which the photo is taken. In this paper, a web based cross platform mobile framework based on deep neural networks for autonomous identification of the historical landmarks of Iran is presented. The images recorded by the mobile phone is sent to the decentralized servers in order to be processed and the information about the landmark is determined and transferred to the mobile device of the tourist. The proposed framework is evaluated on the tourism attractions of Iran and the experimental results show that the proposed system can recognize the historical landmarks with precision of 95%. © 2019 IEEE
  6. Keywords:
  7. Convolutional neural networks ; Web-based applications ; Cameras ; Deep learning ; Deep neural networks ; Neural networks ; Pattern recognition ; Smartphones ; Websites ; Complex task ; Convolutional neural network ; Cross-platform ; Economic revenue ; Historical landmarks ; Mobile phone cameras ; Smart tourism ; Web-based applications ; Image analysis
  8. Source: 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, 6 March 2019 through 7 March 2019 ; 2019 , Pages 190-194 ; 9781728116211 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/8785975