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2019 | OriginalPaper | Chapter

Fashion Police: Towards Semantic Indexing of Clothing Information in Surveillance Data

Authors : Owen Corrigan, Suzanne Little

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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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.

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previous chapter Exploring the Impact of Training Data Bias on Automatic Generation of Video Captions
next chapter CNN-Based Non-contact Detection of Food Level in Bottles from RGB Images
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Metadata
Title
Fashion Police: Towards Semantic Indexing of Clothing Information in Surveillance Data
Authors
Owen Corrigan
Suzanne Little
Copyright Year
2019
Book
MultiMedia Modeling

Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05710-7_16