Human gait is an important biometric feature for automatic people recognition. Biometric methodologies are generally intrusive and require the collaboration of the subject in order to perform accurate data acquisition. Gait, instead, can be captured at a distance and without collaboration. This makes it an unobtrusive method for recognizing people in video surveillance systems. In this paper we propose a method to characterize walking gait using three-dimensional skeleton information acquired by the Microsoft Kinect sensor. A set of static and dynamic features correlated to human gait are extracted by the estimated skeleton joint positions. Moreover, we proposed to describe joints positions in a coordinate reference system oriented according to the walking direction to better represents the movement of human body. Using unsupervised clustering over a set of 20 subjects we analyze the effectiveness of the selected features in discriminating people gaits. It turns out that a few dynamic parameters involving the movement of knees, elbows and head are good candidates for robust gait characterization.

Gait characterization using dynamic skeleton acquisition

GIANARIA, ELENA;BALOSSINO, Nello;GRANGETTO, Marco;LUCENTEFORTE, Maurizio
2013

Abstract

Human gait is an important biometric feature for automatic people recognition. Biometric methodologies are generally intrusive and require the collaboration of the subject in order to perform accurate data acquisition. Gait, instead, can be captured at a distance and without collaboration. This makes it an unobtrusive method for recognizing people in video surveillance systems. In this paper we propose a method to characterize walking gait using three-dimensional skeleton information acquired by the Microsoft Kinect sensor. A set of static and dynamic features correlated to human gait are extracted by the estimated skeleton joint positions. Moreover, we proposed to describe joints positions in a coordinate reference system oriented according to the walking direction to better represents the movement of human body. Using unsupervised clustering over a set of 20 subjects we analyze the effectiveness of the selected features in discriminating people gaits. It turns out that a few dynamic parameters involving the movement of knees, elbows and head are good candidates for robust gait characterization.
IEEE 15th International Workshop on Multimedia Signal Processing (MMSP)
Pula, Italia
30/9/2013
IEEE 15th International Workshop on Multimedia Signal Processing
IEEE
440
445
9781479901258
E. Gianaria; N. Balossino; M. Grangetto; M. Lucenteforte
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/140701
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