BALÁŽIA, Michal a Petr SOJKA. Gait Recognition from Motion Capture Data. ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. New York, USA: ACM, 2018, roč. 14, 1s, s. 1-18. ISSN 1551-6857. Dostupné z: https://dx.doi.org/10.1145/3152124. |
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@article{1390685, author = {Balážia, Michal and Sojka, Petr}, article_location = {New York, USA}, article_number = {1s}, doi = {http://dx.doi.org/10.1145/3152124}, keywords = {gait recognition}, language = {eng}, issn = {1551-6857}, journal = {ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans}, note = {ACM TOMM}, title = {Gait Recognition from Motion Capture Data}, url = {https://doi.org/10.1145/3152124}, volume = {14}, year = {2018} }
TY - JOUR ID - 1390685 AU - Balážia, Michal - Sojka, Petr PY - 2018 TI - Gait Recognition from Motion Capture Data JF - ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans VL - 14 IS - 1s SP - 1-18 EP - 1-18 PB - ACM SN - 15516857 N1 - ACM TOMM KW - gait recognition UR - https://doi.org/10.1145/3152124 L2 - https://doi.org/10.1145/3152124 N2 - Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition. ER -
BALÁŽIA, Michal a Petr SOJKA. Gait Recognition from Motion Capture Data. \textit{ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans}. New York, USA: ACM, 2018, roč.~14, 1s, s.~1-18. ISSN~1551-6857. Dostupné z: https://dx.doi.org/10.1145/3152124.
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