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Fashion police: towards semantic indexing of clothing information In surveillance data

Corrigan, Owen orcid logoORCID: 0000-0002-1840-982X and Little, Suzanne orcid logoORCID: 0000-0003-3281-3471 (2018) Fashion police: towards semantic indexing of clothing information In surveillance data. In: Conference on Multimedia Modeling, 8-11 Jan 2019, Thessaloniki, Greece. ISBN 978-3-030-05709-1

Abstract
Indexing and retrieval of clothing based on style, similarity and colour has been extensively studied in the field of fashion with good results. However, retrieval of real-world clothing examples based on witness descriptions is of great interest in for security and law enforcement applications. Manually searching databases or CCTV footage to identify matching examples is time consuming and ineffective. Therefore we propose using machine learning to automatically index video footage based on general clothing types and evaluate the performance using existing public datasets. The challenge is that these datasets are highly sanitised with clean backgrounds and front-facing examples and are insufficient for training detectors and classifiers for real-world video footage. In this paper we highlight the deficiencies of using these datasets for security applications and propose a methodology for collecting a new dataset, as well as examining several ethical issues.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: MultiMedia Modeling Part 1 (MMM 2019). Lecture Notes in Computer Science 11295. Springer. ISBN 978-3-030-05709-1
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/978-3-030-05710-7_16
Copyright Information:© 2019 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:EU's Horizon 2020, Project ASGARD grant agreement number 700381, Science Foundation Ireland Grant Number SFI/12/RC/2289.
ID Code:22713
Deposited On:17 Oct 2018 14:45 by Owen Corrigan . Last Modified 11 Feb 2019 14:03
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