Continuous Swimming Motion Detection Based on Convolutional Neural Network
In many sports,it is useful to analyze video of athletes in action.In swimming,stroke rate is a common metric used by coaches;detailed marking of each stroke is required.Here we propose a method to automatically detect discrete events in continuous video by using convolutional neural networks.The method learns a mapping from frame windows to points on a smoothed one-dimensional target signal,the peak represents the position of a stroke,evaluation is treated as a sliding window.It is demonstrated that the proposed method works well on the task of detecting swimming movements in the wild.And the ex-periments show that the proposed method can meet the actual needs.
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