Data-Augmentation for Reducing Dataset Bias in Person Re-identification

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Abstract

In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.
Original languageEnglish
Title of host publicationProceedings of 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)978­1­4673­7632­7
DOIs
Publication statusPublished - Aug 2015
Event 3rd AMMDS Workshop - AVSS 2015: Activity Monitoring by Multiple Distributed Sensing - Karlsruhe, Germany
Duration: 25 Aug 2015 → …

Workshop

Workshop 3rd AMMDS Workshop - AVSS 2015: Activity Monitoring by Multiple Distributed Sensing
CountryGermany
CityKarlsruhe
Period25/08/2015 → …

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  • Cite this

    McLaughlin, N., Martinez del Rincon, J., & Miller, P. (2015). Data-Augmentation for Reducing Dataset Bias in Person Re-identification. In Proceedings of 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/AVSS.2015.7301739