A privacy-compliant approach to responsible dataset utilisation for vehicle re-identification

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Qian, Yan
Barthélemy, Johan
Du, Bo
Shen, Jun
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2024
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Abstract

Modern surveillance systems increasingly adopt artificial intelligence (AI) for their automated reasoning capacities. While using AI can save manual labour and improve efficiency, addressing the ethical concerns of such technologies is often overlooked. One of these AI application technologies is vehicle re-identification - the process of identifying vehicles through multiple cameras. If vehicle re-identification is going to be used on and with humans, we need to ensure the ethical and trusted operations of these systems. Creating reliable re-identification models relies on large volumes of training datasets. This paper identifies, for the first time, limitations in a commonly used training dataset that impacts the research in vehicle re-identification. The limitations include noises due to writings on images and, most importantly, visible faces of drivers or passengers. There is an issue if facial recognition is indirectly performed by these black box models as a by-product. To this end, we propose an approach using an image-to-image translation model to generate less noisy training data that can guarantee the privacy and anonymity of people for vehicle re-identification.

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Digital Transportation and Safety

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© 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.

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This publication has been entered in Griffith Research Online as an advance online version.

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Qian, Y; Barthélemy, J; Du, B; Shen, J, A privacy-compliant approach to responsible dataset utilisation for vehicle re-identification, Digital Transportation and Safety, 2024

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