Počet záznamů: 1  

Interpretable Gait Recognition by Granger Causality

  1. 1.
    SYSNO ASEP0567047
    Druh ASEPC - Konferenční příspěvek (mezinárodní konf.)
    Zařazení RIVD - Článek ve sborníku
    NázevInterpretable Gait Recognition by Granger Causality
    Tvůrce(i) Balazia, M. (FR)
    Hlaváčková-Schindler, Kateřina (UIVT-O) RID, ORCID, SAI
    Sojka, P. (CZ)
    Plant, C. (AT)
    Celkový počet autorů4
    Zdroj.dok.2022 26th International Conference on Pattern Recognition (ICPR). - Piscataway : IEEE, 2022 - ISSN 1051-4651 - ISBN 978-1-6654-9063-4
    Rozsah strans. 1069-1075
    Poč.str.7 s.
    Forma vydáníTištěná - P
    AkceICPR 2022: International Conference on Pattern Recognition /26./
    Datum konání21.08.2022 - 25.08.2022
    Místo konáníMontréal
    ZeměCA - Kanada
    Typ akceWRD
    Jazyk dok.eng - angličtina
    Země vyd.US - Spojené státy americké
    Klíč. slovaMeasurement ; Analytical models ; Three-dimensional displays ; Neural networks ; Video surveillance ; Skeleton ; Motion capture
    Obor OECDComputer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    CEPGA19-16066S GA ČR - Grantová agentura ČR
    Institucionální podporaUIVT-O - RVO:67985807
    UT WOS000897707601011
    EID SCOPUS85143611376
    DOI10.1109/ICPR56361.2022.9956624
    AnotaceWhich joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the standardized motion capture format, constituting a set of 3D joint spatial trajectories, is envisaged as a causal system of joints interacting in time. We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait. We evaluate eleven distance functions in the GGM feature space by established classification and class-separability evaluation metrics. Our experiments indicate that, depending on the metric, the most appropriate distance functions for the GGM are the total norm distance and the Ky-Fan 1-norm distance. Experiments also show that the GGM is able to detect the most discriminative joint interactions and that it outperforms five related interpretable models in correct classification rate and in Davies-Bouldin index. The proposed GGM model can serve as a complementary tool for gait analysis in kinesiology or for gait recognition in video surveillance.
    PracovištěÚstav informatiky
    KontaktTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Rok sběru2023
Počet záznamů: 1  

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