Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
A Visual Tracking Algorithm for Real Time People Detection
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
An Unsupervised Approach for Segmentation and Clustering of Soccer Players
IMVIP '07 Proceedings of the International Machine Vision and Image Processing Conference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Behavior classification by eigendecomposition of periodic motions
Pattern Recognition
Human action recognition using distribution of oriented rectangular patches
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Behavior histograms for action recognition and human detection
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
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Human action recognition is an important research area in the field of computer vision having a great number of real-world applications. This paper presents a multi-view action recognition framework able to extract human silhouette clues from different synchronized static cameras and then to validate them by analyzing scene dynamics. Two different algorithmic procedures were introduced: the first one performs, in each acquired image, the neural recognition of the human body configuration by using a novel mathematical tool called Contourlet transform. The second procedure performs, instead, 3D ball and player motion analysis. The outcomes of both procedures are then merged to accomplish the final player action recognition task. Experiments were carried out on several image sequences acquired during some matches of the Italian "Serie A" soccer championship.