You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00097675" target="_blank" >RIV/00216224:14330/17:00097675 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/document/8272700/" target="_blank" >http://ieeexplore.ieee.org/document/8272700/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BTAS.2017.8272700" target="_blank" >10.1109/BTAS.2017.8272700</a>

Result language

angličtina

Original language name

You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data

Original language description

This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video surveillance scenario: (1) the walkers do not cooperate in providing learning data to establish their identities and (2) the data are often noisy or incomplete. We show that only a few examples of human gait cycles are required to learn a projection of raw MoCap data onto a low-dimensional sub-space where the identities are well separable. Latent features learned by Maximum Margin Criterion (MMC) method discriminate better than any collection of geometric features. The MMC method is also highly robust to noisy data and works properly even with only a fraction of joints tracked. The overall workflow of the design is directly applicable for a day-to-day operation based on the available MoCap technology and algorithms for gait analysis. In the concept we introduce, a walker's identity is represented by a cluster of gait data collected at their incidents within the surveillance system: They are how they walk.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

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

Article name in the collection

Proceedings of the 3rd IEEE/IAPR International Joint Conference on Biometrics (IJCB 2017)

ISBN

9781538611241

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

208-215

Publisher name

IEEE

Place of publication

USA

Event location

Denver, USA

Event date

Jan 1, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000426973200026