Siamese Spatio-temporal convolutional neural network for stroke classification in Table Tennis games
Abstract
This work presents a Table Tennis stroke classification approach through a siamese spatio-temporal convolutional neural network-SSTCNN. The videos are recorded at 120 frames per second with players performing in natural conditions. The frames are extracted, resized and processed to compute the optical flow. From the optical flow, a region of interest-ROI-is inferred. The SSTCNN is then feed by RGB and optical flow ROIs stream to give a probabilistic classification over all the table tennis strokes.
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