Hotels-50K: A Global Hotel Recognition Dataset


  • Abby Stylianou Washington University in St. Louis
  • Hong Xuan George Washington University
  • Maya Shende George Washington University
  • Jonathan Brandt Adobe Research
  • Richard Souvenir Temple University
  • Robert Pless George Washington University



Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.




How to Cite

Stylianou, A., Xuan, H., Shende, M., Brandt, J., Souvenir, R., & Pless, R. (2019). Hotels-50K: A Global Hotel Recognition Dataset. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 726-733.



AAAI Special Technical Track: AI for Social Impact