Authors:
Marco Leo
;
Tiziana D’Orazio
;
Paolo Spagnolo
and
Pier Luigi Mazzeo
Affiliation:
Italian National Council Research, Italy
Keyword(s):
Human pose estimation, Contourlet transform, Neural networks, Soccer player activity recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Vision
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
Abstract:
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 introducing advanced reasonings about scene dynamics. Two different algorithmic procedures have been introduced: the first one performs, in each acquired image, the neural recognition of the human body configuration by using a novel mathematic tool named Contourlet transform. The second procedure performs, instead, 3D ball and player motion analysis. The outcomes of both procedures are then properly merged to accomplish the final player activity recognition task. Experimental results were carried out on several image sequences acquired during some matches of the Italian Serie A soccer championship.