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

• Published in: IET biometrics Vol. 7; no. 5; pp. 415 - 422
• Main Authors: Switonski, Adam, Krzeszowski, Tomasz, Josinski, Henryk, Kwolek, Bogdan, Wojciechowski, Konrad
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.09.2018; John Wiley & Sons, Inc
• Subjects: 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
• DOI: 10.1049/iet-bmt.2017.0134

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.