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Authors: Tanwi Mallick ; Ankit Khedia ; Partha Pratim Das and Arun Kumar Majumdar

Affiliation: Indian Institute of Technology, India

ISBN: 978-989-758-175-5

Keyword(s): Gait Recognition, Kinect Skeleton Stream.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Enterprise Information Systems ; Human and Computer Interaction ; Human-Computer Interaction ; Motion, Tracking and Stereo Vision ; Tracking and Visual Navigation

Abstract: Recognizing persons from gait has attracted attention in computer vision research for over a decade and a half. To extract the motion information in gait, researchers have either used wearable markers or RGB videos. Markers naturally offer good accuracy and reliability but has the disadvantage of being intrusive and expensive. RGB images, on the other hand, need high processing time to achieve good accuracy. Advent of low-cost depth data from Kinect 1.0 and its human-detection and skeleton-tracking abilities have opened new opportunities in gait recognition. Using skeleton data it gets cheaper and easier to get the body-joint information that can provide critical clue to gait-related motions. In this paper, we attempt to use the skeleton stream from Kinect 1.0 for gait recognition. Various types of gait features are extracted from the joint-points in the stream and the appropriate classifiers are used to compute effective matching scores. To test our system and compare performance, we create a benchmark data set of 5 walks each for 29 subjects and implement a state-of-the-art gait recognizer for RGB videos. Tests show a moderate accuracy of 65% for our system. This is low compared to the accuracy of RGB-based method (which achieved 83% on the same data set) but high compared to similar skeleton-based approaches (usually below 50%). Further we compare execution time of various parts of our system to highlight efficiency advantages of our method and its potential as a real-time recogniser if an optimized implementation can be done. (More)

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Paper citation in several formats:
Mallick, T.; Khedia, A.; Das, P.; Das, P. and Majumdar, A. (2016). Fast Gait Recognition from Kinect Skeletons.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 340-347. DOI: 10.5220/0005713903400347

@conference{visapp16,
author={Tanwi Mallick. and Ankit Khedia. and Partha Pratim Das. and Partha Pratim Das. and Arun Kumar Majumdar.},
title={Fast Gait Recognition from Kinect Skeletons},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={340-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005713903400347},
isbn={978-989-758-175-5},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Fast Gait Recognition from Kinect Skeletons
SN - 978-989-758-175-5
AU - Mallick, T.
AU - Khedia, A.
AU - Das, P.
AU - Das, P.
AU - Majumdar, A.
PY - 2016
SP - 340
EP - 347
DO - 10.5220/0005713903400347

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