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

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

doi:10.1117/12.2559441

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

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

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%.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available