The invention discloses an action similarity evaluation method based on
small sample learning. The method comprises the steps of establishing a data preprocessing model, Training model, Test model, ahuman
body posture estimation model is adopted to extract a
human body overall skeleton motion video and each
joint point position; Excluding background interference, the action of the person is splitaccording to the
joint point position; setting a sampling pixel value and a sampling interval; intercepting to obtain a sampling video comprising a
human body integral skeleton motion video and a
joint action taking each
joint point as a center; the sampling video combines local information and
global information, after data preprocessing, a rewritten triple
loss function is used for training, finally video data are mapped to a cosine space, the
cosine distance is calculated, and the overall action of a person in the video and the similarity degree result of all joints are output. According to the method, a good action
feature mapping model can be learned only by using few samples, so that a good action similarity result is obtained.