To build a successful supervised learning model for action recognition a large amount of training data needs to be labeled first. Labeling is normally done manually and it is a tedious and time-consuming task, especially in the case of video footage, when each individual athlete performing a given action should be labeled. To minimize the manual labor, we propose a Mask R-CNN and Optical flow based method to determine the active players who perform a given action among all players presented on the scene. The Mask R-CNN is a deep learning object recognition method used for player detection and optical flow measures player activity. Combining both methods ensures tracking and labeling of active players in handball video sequences. The method was successfully tested on a dataset of handball practice videos recorded in the wild.
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