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A Step Towards Explainable Person Re-identification Rankings

Specker, Andreas ORCID iD icon

Abstract (englisch):

More and more video and image data is available to security authorities that can help solve crimes. Since manual analysis is time-consuming, algorithms are needed that support e.g. re-identification of persons. However, person re-identification approaches solely output image rank lists but do not provide an explanation for the results.

In this work, two concepts are proposed to explain person re-identification rankings and a qualitative evaluation is conducted. Both approaches are based on a multi-task convolutional neural network which outputs feature vectors for person re-identification and simultaneously recognizes a person’s semantic attributes. Analyses of the learned weights and the outputs of the attribute classifier are used to generate the explanations.

The results of the conducted experiments indicate that both approaches are suitable to improve the comprehensibility of person re-identification rankings.

Verlagsausgabe §
DOI: 10.5445/IR/1000135219
Veröffentlicht am 12.07.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-3-7315-1091-8
KITopen-ID: 1000135219
Erschienen in Proceedings of the 2020 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. Ed.: J. Beyerer; T. Zander
Verlag KIT Scientific Publishing
Seiten 107-121
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 51
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