Gait recognition on the basis of markerless motion tracking and DTW transform

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 markerles...

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Veröffentlicht in:IET biometrics Jg. 7; H. 5; S. 415 - 422
Hauptverfasser: Switonski, Adam, Krzeszowski, Tomasz, Josinski, Henryk, Kwolek, Bogdan, Wojciechowski, Konrad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage The Institution of Engineering and Technology 01.09.2018
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Biomechanics
Classification
classification method
DTW transform
dynamic time warping transform
Gait
gait analysis
Gait recognition
Identification
image classification
image motion analysis
image sequences
Information processing
k‐nearest neighbours algorithm
learning (artificial intelligence)
markerless motion capture system
markerless motion tracking
Methods
Motion capture
motion sequences
multidimensional time series
object tracking
Research Article
Sequences
skeleton‐driven point clouds
Time measurement
Time series
Tracking
transforms
view‐invariant gait recognition
gait recognition
image classification
dynamic time warping transform
k-nearest neighbours algorithm
transforms
time series
markerless motion capture system
multidimensional time series
gait analysis
DTW transform
image motion analysis
markerless motion tracking
skeleton-driven point clouds
object tracking
classification method
motion sequences
learning (artificial intelligence)
image sequences
view-invariant gait recognition
ISSN:2047-4938, 2047-4946, 2047-4946
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2047-4938
2047-4946
2047-4946
DOI:10.1049/iet-bmt.2017.0134