Open AccessGait recognition on the basis of markerless motion tracking and DTW transform
Author(s) -
Switonski Adam,
Krzeszowski Tomasz,
Josinski Henryk,
Kwolek Bogdan,
Wojciechowski Konrad
Publication year - 2018
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2017.0134
Subject(s) - artificial intelligence , computer science , computer vision , point cloud , dynamic time warping , gait , motion capture , basis (linear algebra) , tracking (education) , pattern recognition (psychology) , match moving , motion (physics) , mathematics , physiology , psychology , pedagogy , geometry , biology
In this study, a framework for view‐invariant gait recognition on the basis of markerless motion tracking and dynamic time warping (DTW) transform is presented. The system consists of a proposed markerless motion capture system as well as introduced classification method of mocap data. The markerless system estimates the three‐dimensional locations of skeleton driven joints. Such skeleton‐driven point clouds represent poses over time. The authors align point clouds in every pair of frames by calculating the minimal sum of squared distances between the corresponding joints. A point cloud distance measure with temporal context has been utilised in k ‐nearest neighbours algorithm to compare time instants of motion sequences. To enhance the generalisation of the recognition and to shorten the processing time, for every individual a single multidimensional time series among several multidimensional time series describing the individual's gait is established. The correct classification rate has been determined on the basis of a real dataset of human gait. It contains 230 gait cycles of 22 subjects. The tracking results on the basis of markerless motion capture are referenced to Vicon system, whereas the achieved accuracies of recognition are compared with the ones obtained by DTW that is based on rotational data.