  Real time processing for teamwork action recognition is a challenge, due to
complex computational models to achieve high system performance. Hence, this
paper proposes a framework based on Graphical Processing Units (GPUs) to
achieve a significant speed up in the performance of role based activity
recognition of teamwork. The framework can be applied in various fields,
especially athletic and military applications. Furthermore, the framework can
be customized for many action recognition applications. The paper presents the
stages of the framework where GPUs are the main tool for performance
improvement. The speedup is achieved by performing video processing and Machine
learning algorithms on GPU. Video processing and machine learning algorithms
covers all computations involved in our framework. Video processing tasks on
involves GPU implementation of Motion detection, segmentation and object
tracking algorithms. In addition, our framework is integrated with GPUCV, a GPU
version of OpenCV functions. Machine learning tasks are supported under our
framework with GPU implementations of Support Vector Machine (SVM) for object
classification and feature discretization, Hidden Marcov Model (HMM) for
activity recognition phase, and ID3 algorithm for role recognition of team
members. The system was tested against UC-Teamwork dataset and speedup of 20X
has been achieved on NVidia 9500GT graphics card (32 500MHZ processors).
