Human Gait Recognition from Motion Capture Data in Signature Poses

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00095906" target="_blank" >RIV/00216224:14330/17:00095906 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/document/7847562/" target="_blank" >http://ieeexplore.ieee.org/document/7847562/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1049/iet-bmt.2015.0072" target="_blank" >10.1049/iet-bmt.2015.0072</a>

Result language

angličtina

Original language name

Human Gait Recognition from Motion Capture Data in Signature Poses

Original language description

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -- there have been many good geometric features designed -- but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IET Biometrics

ISSN

2047-4938

e-ISSN

—

Volume of the periodical

6

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

129-137

UT code for WoS article

000396411600010

EID of the result in the Scopus database

2-s2.0-85012110462