The 2021 Hotel-ID to Combat Human Trafficking Competition Dataset

TL;DR

This paper introduces the 2021 Hotel-ID dataset, a collection of low-quality, crowd-sourced hotel room images aimed at improving hotel recognition for human trafficking investigations, highlighting the dataset's potential to advance research in this critical area.

Contribution

The paper presents the first publicly available dataset tailored for hotel recognition in human trafficking investigations, addressing challenges of low quality and occlusion in investigative images.

Findings

Dataset includes diverse, crowd-sourced hotel images similar to investigative conditions.

Facilitates development of models for accurate hotel recognition in challenging scenarios.

Raises awareness and provides a benchmark for future research in trafficking-related hotel identification.

Abstract

Hotel recognition is an important task for human trafficking investigations since victims are often photographed in hotel rooms. Identifying these hotels is vital to trafficking investigations since they can help track down current and future victims who might be taken to the same places. Hotel recognition is a challenging fine grained visual classification task as there can be little similarity between different rooms within the same hotel, and high similarity between rooms from different hotels (especially if they are from the same chain). Hotel recognition to combat human trafficking poses additional challenges as investigative images are often low quality, contain uncommon camera angles and are highly occluded. Here, we present the 2021 Hotel-ID dataset to help raise awareness for this problem and generate novel approaches. The dataset consists of hotel room images that have been crowd-sourced and uploaded through the TraffickCam mobile application. The quality of these images is similar to investigative images and hence models trained on these images have good chances of accurately narrowing down on the correct hotel.