Paper Info
Title
Unsupervised Temporal Segmentation of Repetitive Human Actions Based on Kinematic Modeling and Frequency Analysis
Abstract
In this paper, we propose a method for temporal segmentation of human repetitive actions based on frequency analysis of kinematic parameters, zero-velocity crossing detection, and adaptive k-means clustering. Since the human motion data may be captured with different modalities which have different temporal sampling rate and accuracy (e.g., Optical motion capture systems vs. Microsoft Kinect), we first apply a generic full-body kinematic model with an unscented Kalman filter to convert the motion data into a unified representation that is robust to noise. Furthermore, we extract the most representative kinematic parameters via the primary frequency analysis. The sequences are segmented based on zero-velocity crossing of the selected parameters followed by an adaptive k-means clustering to identify the repetition segments. Experimental results demonstrate that for the motion data captured by both the motion capture system and the Microsoft Kinect, our proposed algorithm obtains robust segmentation of repetitive action sequences.
Year
DOI
Venue
2015
10.1109/3DV.2015.69
3DV
Keywords
Field
DocType
unsupervised temporal segmentation,kinematic modeling,zero-velocity crossing detection,adaptive k-means clustering,human motion data,generic full-body kinematic model,unscented Kalman filter,primary frequency analysis,motion capture system,Microsoft Kinect
Motion capture,Computer vision,Kinematics,Segmentation,Computer science,Sampling (signal processing),Kalman filter,Artificial intelligence,Frequency analysis,Hidden Markov model,Cluster analysis
Journal
Volume
Citations 
PageRank 
abs/1512.04115
9
0.59
References 
Authors
13
4
Name
Order
Citations
PageRank
Qifei Wang1796.67
Gregorij Kurillo249434.71
Ferda Ofli342627.96
Ruzena Bajcsy43621869.56