Advanced Player Activity Recognition by Integrating Body Posture and Motion Information

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.