Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data

In this paper, a real-time tracking-based approach to human action recognition is proposed. The method receives as input depth map data streams from a single kinect sensor. Initially, a skeleton-tracking algorithm is applied. Then, a new action representation is introduced, which is based on the cal...

Full description

Saved in:
Bibliographic Details
Published inMultiMedia Modeling pp. 473 - 483
Main Authors Papadopoulos, Georgios Th, Axenopoulos, Apostolos, Daras, Petros
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Action recognition
action representation
depth map
kinect
skeleton-tracking
Online AccessGet full text

Cover

More Information
Summary:In this paper, a real-time tracking-based approach to human action recognition is proposed. The method receives as input depth map data streams from a single kinect sensor. Initially, a skeleton-tracking algorithm is applied. Then, a new action representation is introduced, which is based on the calculation of spherical angles between selected joints and the respective angular velocities. For invariance incorporation, a pose estimation step is applied and all features are extracted according to a continuously updated torso-centered coordinate system; this is different from the usual practice of using common normalization operators. Additionally, the approach includes a motion energy-based methodology for applying horizontal symmetry. Finally, action recognition is realized using Hidden Markov Models (HMMs). Experimental results using the Huawei/3DLife 3D human reconstruction and action recognition Grand Challenge dataset demonstrate the efficiency of the proposed approach.
ISBN:3319041134
9783319041131
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-04114-8_40