View indepedent human movement recognition from multi-view video exploiting a circular invariant posture representation

Authors:
Nikolaos Gkalelis;Nikos Nikolaidis;Ioannis Pitas
Affiliations:
Informatics and Telematics Institute, Centre for Research and Technology Hellas and Department of Informatics, Aristotle University of Thessaloniki, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas and Department of Informatics, Aristotle University of Thessaloniki, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas and Department of Informatics, Aristotle University of Thessaloniki, Greece
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 10
Cited 6

The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
A real-time interactive multi-view video system

Proceedings of the 13th annual ACM international conference on Multimedia
View Invariance for Human Action Recognition

International Journal of Computer Vision
HMM-based Human Action Recognition Using Multiview Image Sequences

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Free viewpoint action recognition using motion history volumes

Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Cross-View Action Recognition from Temporal Self-similarities

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
View-invariant modeling and recognition of human actions using grammars

WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Quantifying and recognizing human movement patterns from monocular video Images-part I: a new framework for modeling human motion

IEEE Transactions on Circuits and Systems for Video Technology
Combining Fuzzy Vector Quantization With Linear Discriminant Analysis for Continuous Human Movement Recognition

IEEE Transactions on Circuits and Systems for Video Technology
Machine Recognition of Human Activities: A Survey

IEEE Transactions on Circuits and Systems for Video Technology

Anthropocentric video analysis for film and games postproduction

Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Learning human identity using view-invariant multi-view movement representation

BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Human action recognition using multiple views: a comparative perspective on recent developments

J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Motion history of skeletal volumes and temporal change in bounding volume fusion for human action recognition

MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
A survey of video datasets for human action and activity recognition

Computer Vision and Image Understanding
Learning sparse representations for view-independent human action recognition based on fuzzy distances

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper a novel method for view independent human movement representation and recognition, exploiting the rich information contained in multi-view videos, is proposed. The binary masks of a multi-view posture image are first vectorized, concatenated and the view correspondence problem between train and test samples is solved using the circular shift invariance property of the discrete Fourier transform (DFT) magnitudes. Then, using fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA), different movements are represented and classified. This method allows view independent movement recognition, without the use of calibrated cameras, a-priori view correspondence information or 3D model reconstruction. A multiview video database has been constructed for the assessment of the proposed algorithm. Evaluation of this algorithm on the new database, shows that it is particularly efficient and robust, and can achieve good recognition performance.