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@inproceedings{1390687,
author = {Balážia, Michal and Sojka, Petr},
address = {USA},
booktitle = {Proceedings of the 3rd IEEE/IAPR International Joint Conference on Biometrics (IJCB 2017)},
doi = {http://dx.doi.org/10.1109/BTAS.2017.8272700},
editor = {Ross Beveridge, Liang Wang, Mark Nixon},
keywords = {gait recognition},
howpublished = {elektronická verze "online"},
language = {eng},
location = {USA},
isbn = {978-1-5386-1124-1},
pages = {208-215},
publisher = {IEEE},
title = {You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data},
url = {http://ieeexplore.ieee.org/document/8272700/},
year = {2017}
}
TY - JOUR
ID - 1390687
AU - Balážia, Michal - Sojka, Petr
PY - 2017
TI - You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
PB - IEEE
CY - USA
SN - 9781538611241
KW - gait recognition
UR - http://ieeexplore.ieee.org/document/8272700/
L2 - http://ieeexplore.ieee.org/document/8272700/
N2 - 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.
ER -
BALÁŽIA, Michal a Petr SOJKA. You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data. In Ross Beveridge, Liang Wang, Mark Nixon. \textit{Proceedings of the 3rd IEEE/IAPR International Joint Conference on Biometrics (IJCB 2017)}. USA: IEEE. s.~208-215. ISBN~978-1-5386-1124-1. doi:10.1109/BTAS.2017.8272700. 2017.
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