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High Performance Moves Recognition and Sequence Segmentation

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We present a discriminative key pose-based approach for moves recognition and segmentation of training sequences for high performance sports. Compared with daily human gestures, moves in high performance sports are faster and have low inter-class variability, which produce noisy features and ambiguity. Our approach combines a robust filtering strategy to select frames composed of discriminative poses (key poses) and the discriminative Latent-Dynamic Conditional
Random Fields model to predict a label for each frame from the training sequence.

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