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.
@article{arxiv.1701.00995,
title = {An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods},
author = {Michal Balazia and Petr Sojka},
journal= {arXiv preprint arXiv:1701.00995},
year = {2022}
}
Comments
Preprint. Full paper published at the 1st IAPR Workshop on Proceedings of Reproducible Research in Pattern Recognition (RRPR), Cancun, Mexico, December 2016. 13 pages. arXiv admin note: text overlap with arXiv:1609.06936