The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A data-driven approach to quantifying natural human motion
ACM SIGGRAPH 2005 Papers
Learning silhouette features for control of human motion
ACM Transactions on Graphics (TOG)
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
IEEE Transactions on Image Processing
CCTV Video Analytics: Recent Advances and Limitations
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Drawing Motion without Understanding It
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
Human body pose estimation from still images and video frames
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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We introduce a large body of virtual human action silhouette (ViHASi) data generated recently for the purpose of evaluating a family of action recognition methods. These are the silhouette-based human action recognition methods. This synthetic multi-camera video data-set consists of 20 action classes, 9 actors and up to 40 synchronized perspective cameras. The data-set has been made available online for other researchers to use. In order to demonstrate the usefulness of the ViHASi data we make use of our recent action recognition method that is simple and relatively fast. Moreover, to deal with long video sequences containing several action samples, a practical temporal segmentation algorithm is introduced and tested that is tightly coupled with the action recognition method used. Our experimental methodologies provides a reasonable platform for quantitatively comparing silhouette-based action recognition methods.