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