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MediaEval 2019: LRCNs for Stroke Detection in Table Tennis

Recognizing actions in videos is one of the most widely researched tasks in video analytics. Sports action recognition is one such work that has been extensively researched in order to make strategic decisions in athletic training. We present a model to classify strokes made by table tennis players as a part of the 2019 MediaEval Challenge. Our approach extracts features into a spatio-temporal model trained on the MediaEval Sports Video Classification dataset to detect the move made.