The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
HMM-Based Action Recognition Using Contour Histograms
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Recognizing Human Actions Using Silhouette-based HMM
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A survey on vision-based human action recognition
Image and Vision Computing
Human action recognition using distribution of oriented rectangular patches
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Multiview activity recognition in smart homes with spatio-temporal features
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
On efficient use of multi-view data for activity recognition
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Human activity analysis: A review
ACM Computing Surveys (CSUR)
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Multi-view human action recognition system employing 2DPCA
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
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
Silhouette-Based method for object classification and human action recognition in video
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Human action recognition optimization based on evolutionary feature subset selection
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Optimal joint selection for skeletal data from RGB-D devices using a genetic algorithm
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Evolutionary joint selection to improve human action recognition with RGB-D devices
Expert Systems with Applications: An International Journal
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This paper presents a novel multi-view human action recognition approach based on a bag-of-key-poses. In the case of multi-view scenarios, it is especially difficult to perform accurate action recognition that still runs at an admissible recognition speed. The presented method aims to fill this gap by combining a silhouette-based pose representation with a simple, yet effective multi-view learning approach based on Model Fusion. Action classification is performed through efficient sequence matching and by the comparison of successive key poses which are evaluated on both feature similarity and match relevance. Experimentation on the MuHAVi dataset shows that the method outperforms currently available recognition rates and is exceptionally robust to actor-variance. Temporal evaluation confirms the method's suitability for real-time recognition.