Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Surveillance of Human Activity
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
On the use of Anthropometry in the Invariant Analysis of Human Actions
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Exploring the Space of a Human Action
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
View invariant gesture recognition using the CSEM SwissRanger SR-2 camera
International Journal of Intelligent Systems Technologies and Applications
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Editorial: Hybrid learning machines
Neurocomputing
Recognizing Human Actions Using Silhouette-based HMM
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A survey on vision-based human action recognition
Image and Vision Computing
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
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
Fusion of single view soft k-NN classifiers for multicamera human action recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
An efficient approach for multi-view human action recognition based on bag-of-key-poses
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Hi-index | 0.01 |
This paper presents a distributed system for the recognition of human actions using views of the scene grabbed by different cameras. 2D frame descriptors are extracted for each available view to capture the variability in human motion. These descriptors are projected into a lower dimensional space and fed into a probabilistic classifier to output a posterior distribution of the action performed according to the descriptor computed at each camera. Classifier fusion algorithms are then used to merge the posterior distributions into a single distribution. The generated single posterior distribution is fed into a sequence classifier to make the final decision on the performed activity. The system can instantiate different algorithms for the different tasks, as the interfaces between modules are clearly defined. Results on the classification of the actions in the IXMAS dataset are reported. The accuracy of the proposed system is similar to state-of-the-art 3D methods, even though it uses only well-known 2D pattern recognition techniques and does not need to project the data into a 3D space or require camera calibration parameters.