Human Action Recognition using Global Point Feature Histograms and Action Shapes

TitleHuman Action Recognition using Global Point Feature Histograms and Action Shapes
Publication TypeJournal Article
Year of Publication2009
AuthorsRusu, Radu Bogdan., Bandouch, Jan., Meier, Franziska., Essa, Irfan., and Beetz, Michael
JournalAdvanced Robotics journal, Robotics Society of Japan (RSJ)
Keywordsperception
Abstract

This article investigates the recognition of human actions from 3D point clouds that encode the
motions of people acting in sensor-distributed indoor environments.
Data streams are time-sequences of silhouettes extracted from cameras in the environment. From
the 2D silhouette contours we generate space-time streams by continuously aligning and stacking
the contours along the time axis as third spatial dimension.
The space-time stream of an observation sequence is segmented into parts corresponding to
subactions using a pattern matching technique based on suffix trees and interval scheduling. Then,
the segmented space-time shapes are processed by treating the shapes as 3D point clouds and
estimating global point feature histograms for them. The resultant models are clustered using
statistical analysis, and our experimental results indicate that the presented methods robustly derive
di erent action classes. This holds despite large intra-class variance in the recorded datasets due to
performances from di erent persons at di erent time intervals.

URLhttp://files.rbrusu.com/publications/Rusu09RSJ-AR.pdf