Deep Learning Based Illegal Bowling Action Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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The paper introduces EventNet, a two‑stage pipeline that uses 2D keypoints of players, table corners, and the ball to detect key events in table tennis videos. First, a keypoint transformer condenses the pose and ball information into a robust representation; second, a transformer encoder predicts how close each frame is to the next and previous ball‑racket contact using a novel temporal cosine‑like target signal. Experiments on Latte‑MV and TTHQ datasets show high accuracy, with an F1 score of 91.16% and a mean frame deviation of 0.42 on Latte‑MV, and 73.08% / 1.16 on TTHQ.
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