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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Stylianou, Abigail"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["Robert Pless","Yasutaka Furukawa Sanmay Das"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-12-01T08:00:00Z","date_published":"2016-12-01T08:00:00Z","updated_at":"2026-07-24T06:13:05Z","subjects":["computer vision","sex trafficking","scene recognition","localization","image matching","Artificial Intelligence and Robotics","Engineering","Forensic Science and Technology","Physical Sciences and Mathematics"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/198"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/198","href":"https://openscholarship.wustl.edu/eng_etds/198","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/K7J38QX2","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Robert Pless","Yasutaka Furukawa Sanmay Das"]},{"key":"dc:creator","label":"Author","values":["Stylianou, Abigail"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-12-29T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer vision","sex trafficking","scene recognition","localization","image matching","Artificial Intelligence and Robotics","Engineering","Forensic Science and Technology","Physical Sciences and Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/K7J38QX2","https://openscholarship.wustl.edu/eng_etds/198"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Permanent URL: https://doi.org/10.7936/K7J38QX2"]},{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Indoor Scene Localization to Fight Sex Trafficking in Hotels"]}]}],"canonical_facts":{"dc:contributor":["Robert Pless","Yasutaka Furukawa Sanmay Das"],"dc:creator":["Stylianou, Abigail"],"dc:date.available":["2016-12-29T08:00:00Z"],"dc:description":["Permanent URL: https://doi.org/10.7936/K7J38QX2"],"dc:description.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.
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