Improving open-set person re-identification by statistics-driven gallery refinement

31 January 2020 Improving open-set person re-identification by statistics-driven gallery refinement

Tunc Alkanat, Egor Bondarev, Peter H. N. de With

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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330V (2020) https://doi.org/10.1117/12.2559441
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Person re-identification (re-ID) is a valuable tool for multi-camera tracking of persons. Up till now, research on person re-ID has mainly focused on the closed-set case, where a given query is assumed to always have a correct match in the gallery set, which does not hold for practical scenarios. In this study, we explore the open-set person re-ID problem with queries not always included in the gallery set. First, we convert the popular closed-set person re-ID datasets into the open-set scenario. Second, we compare the performances of six state-of-the-art closed-set person re-ID methods under open-set conditions. Third, we investigate the impact of a simple and fast statistics-driven gallery refinement approach on the open-set person re-ID performance. Extensive experimental evaluations show that, gallery refinement increases the performance of existing methods in the low false-accept rate (FAR) region, while simultaneously reducing the computational demands of retrieval. Results show an average detection and identification rate (DIR) increase of 7.91% and 3.31% on the DukeMTMC-reID and Market1501 datasets, respectively, for an FAR of 1%.

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Tunc Alkanat, Egor Bondarev, and Peter H. N. de With "Improving open-set person re-identification by statistics-driven gallery refinement", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330V (31 January 2020); https://doi.org/10.1117/12.2559441

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KEYWORDS

Feature extraction

Cameras

Statistical analysis

Image retrieval

Principal component analysis

Electrical engineering

Surveillance systems

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