Detailed Information on Publication Record
2017
You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
BALÁŽIA, Michal and Petr SOJKABasic information
Original name
You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
Authors
BALÁŽIA, Michal (703 Slovakia, guarantor, belonging to the institution) and Petr SOJKA (203 Czech Republic, belonging to the institution)
Edition
USA, Proceedings of the 3rd IEEE/IAPR International Joint Conference on Biometrics (IJCB 2017), p. 208-215, 8 pp. 2017
Publisher
IEEE
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
References:
RIV identification code
RIV/00216224:14330/17:00097675
Organization unit
Faculty of Informatics
ISBN
978-1-5386-1124-1
UT WoS
000426973200026
Keywords (in Czech)
rozpoznávání podle chůze
Keywords in English
gait recognition
Tags
International impact, Reviewed
Změněno: 18/5/2018 12:03, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
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.
Links
MUNI/A/0992/2016, interní kód MU |
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MUNI/A/0997/2016, interní kód MU |
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