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@inproceedings{1359622, author = {Balážia, Michal and Sojka, Petr}, address = {Switzerland}, booktitle = {Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)}, doi = {http://dx.doi.org/10.1007/978-3-319-56414-2_3}, edition = {LNCS 10214}, editor = {Miguel Colom, Bertrand Kerautret and Pascal Monasse}, keywords = {software evaluation framework; gait cycle database; human gait recognition}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Switzerland}, isbn = {978-3-319-56413-5}, pages = {33-47}, publisher = {Springer International Publishing AG}, title = {An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods}, url = {https://doi.org/10.1007/978-3-319-56414-2_3}, year = {2017} }
TY - JOUR ID - 1359622 AU - Balážia, Michal - Sojka, Petr PY - 2017 TI - An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods PB - Springer International Publishing AG CY - Switzerland SN - 9783319564135 KW - software evaluation framework KW - gait cycle database KW - human gait recognition UR - https://doi.org/10.1007/978-3-319-56414-2_3 L2 - https://doi.org/10.1007/978-3-319-56414-2_3 N2 - As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database. ER -
BALÁŽIA, Michal a Petr SOJKA. An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods. In Miguel Colom, Bertrand Kerautret and Pascal Monasse. \textit{Proceedings of the 1st IAPR Workshop on Reproducible Research in Pattern Recognition (RRPR 2016)}. LNCS 10214. Switzerland: Springer International Publishing AG, 2017, s.~33-47. ISBN~978-3-319-56413-5. Dostupné z: https://dx.doi.org/10.1007/978-3-319-56414-2\_{}3.
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