Abstract

Images are key to fighting sex trafficking. They are: (a) used to advertise for sex services,(b) shared among criminal networks, and (c) connect a person in an image to the place where the image was taken. This work explores the ability to link images to indoor places in order to support the investigation and prosecution of sex trafficking. We propose and develop a framework that includes a database of open-source information available on the Internet, a crowd-sourcing approach to gathering additional images, and explore a variety of matching approaches based both on hand-tuned features such as SIFT and learned features using state of the art deep learning approaches. We concentrate on spatio-temporal indexing of hotel rooms, and to date have an index of more than 1.5 million geo-coded images. Our smart-phone app collects contextual information and metadata alongside images.

Committee Chair

Robert Pless

Committee Members

Yasutaka Furukawa Sanmay Das

Comments

Permanent URL: https://doi.org/10.7936/K7J38QX2

Degree

Master of Science (MS)

Author's Department

Computer Science & Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

Winter 12-2016

Language

English (en)

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