Action similarity evaluation method based on small sample learning

A similarity, small sample technology, applied in the field of action similarity evaluation based on small sample learning, can solve the problems of difficult data acquisition and multiple costs, achieve reliable results, and eliminate the interference of background information.

Active Publication Date: 2019-03-26
SUN YAT SEN UNIV
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Problems solved by technology

And because there is a lot of noise in the data, in order to learn good features, the model using the dual-stream architecture needs a lot of data for training to reduce the impact of noise on th

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  • Action similarity evaluation method based on small sample learning
  • Action similarity evaluation method based on small sample learning
  • Action similarity evaluation method based on small sample learning

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[0048] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0049] In this embodiment, an action similarity evaluation method based on small sample learning is provided, and the specific steps are as follows:

[0050] Establish a data preprocessing model: use AlphaPose (human body pose estimation model) to extract human skeleton motion video and joint point positions, such as figure 1 shown. The background information of the data is ignored, which greatly reduces the influence of background noise on the model results, making the features learned by the model focus on the action. Cut out a 100x100 pixel area centered on the joint point, a total of 12 (excl...

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Abstract

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.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an action similarity evaluation method based on small sample learning. Background technique [0002] At present, the main action similarity evaluation method is to use a dual-stream architecture, namely RGB flow and optical flow. Among them, the RGB stream extracts the spatial features of the people in the video, and the optical flow extracts the motion features of the people in the video, and fuses the dual-stream features. The same operation is performed on both videos to obtain two fused dual-stream features, which are input into the decision network to obtain a similarity score. [0003] The model of the dual-stream architecture generally analyzes the entire picture in general, and pays more attention to the global information. This is caused by the structure of the dual-stream model itself, and the similarity evaluation should pay more attention to the local information. And ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/23G06F18/22G06F18/214
Inventor 郑伟诗胡康朱智慧
Owner SUN YAT SEN UNIV
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