An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

Abstract

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 comprises 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 class separability coefficients of feature space and four classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom probe on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database.

You are how you walk: Uncooperative MoCap gait identification for video surveillance with incomplete and noisy data

“…For the purpose of evaluation, we selected the MoCap database of the CMU Graphics Lab [13], which is available under the Creative Commons license. Normalization and extraction of gait cycles from this database is described in [8] and is available for download at [9]. The gait data hold C = 64 walking subjects that performed N = 5 923 samples in total, which resulted in an average of about 83 samples per subject.…”

“…We have based our evaluation on the gait recognition framework [8]. The evaluation focuses on the Phase III in Section 2.…”

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

“…Since motion capture technology became recently more affordable, 3D structure-based gait recognition has attracted more interest from researchers [5,7,32,66]. Recently, a new benchmark data and evaluation protocols for moCap-based gait recognition have been proposed in [6]. As recently shown in [60], marker-less motion capture systems can provide reliable 3D gait kinematics in the sagittal and frontal plane.…”