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Rope skipping counting method based on deep learning

A counting method and deep learning technology, applied in the field of skipping rope counting based on deep learning, can solve the problems of skipping rope counting accuracy error, etc., and achieve the effects of fast counting speed, high application value and high accuracy.

Active Publication Date: 2021-06-25
ZHEJIANG UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The above methods all use intelligent methods based on vision and hearing to count rope skipping, but these methods have certain errors in the accuracy of rope skipping counting

Method used

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  • Rope skipping counting method based on deep learning
  • Rope skipping counting method based on deep learning
  • Rope skipping counting method based on deep learning

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Experimental program
Comparison scheme
Effect test

Embodiment

[0043] see Figure 1 to Figure 9 , the rope skipping counting method based on deep learning of the present embodiment comprises the following steps:

[0044] In the preparatory step, the original video data of the rope skipping action is obtained, and the image data is extracted from the original video data.

[0045] Step S100, read the image, and compress the video size frame by frame.

[0046] Step S200, process the compressed video with the Farneback dense optical flow algorithm, and output a new BGR video. In this embodiment, the frame features during the rising and falling processes are obvious and arranged in time sequence, and the output images are divided into three categories, which are respectively "rising frames" (such as image 3 shown), "still frame" (such as Figure 4 ) and "drop frame" (as in Figure 5 shown).

[0047] Step S300, image classification: use the trained neural network model to classify. Specifically include:

[0048] Step S301, video image p...

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Abstract

The invention relates to a rope skipping counting method based on deep learning, and belongs to the technical field of intelligent fitness sports equipment. Including: 1) obtaining the original video data of the rope skipping action, and extracting the image data from the original video data; 2) reading the video image, and compressing the video size frame by frame; 3) processing the compressed video with the Farneback dense optical flow algorithm, and outputting A new BGR video; 4) Use the trained classification model to classify each frame of the image in the BGR video; 5) Determine the state of skipping rope according to the classification result; 6) Count the skipping rope according to the change of the state of skipping rope; 7) The result is output and show. The obtained image data is preprocessed, and then the trained model is used for classification, and the current motion state is judged according to the classification result, and finally the number of jump rope state changes is counted. The method has high accuracy, fast counting speed and high application value.

Description

technical field [0001] The invention relates to the technical field of intelligent fitness equipment, in particular to a method for counting rope skipping based on deep learning. Background technique [0002] Rope skipping has been used as an important sport for entertainment or competition on many occasions. At the same time, it is also a compulsory subject for the physical examination of elementary and middle school students. However, counting is required in the process of skipping rope. When there are many people, counting each person will greatly delay the precious time of the referee, and sometimes when the rope skipping speed is fast or the referee is inattentive, counting errors are prone to occur. Existing skipping ropes that can count automatically often adopt traditional methods such as machinery, and there are problems such as the ubiquity of counting not being accurate enough, or not being able to count backwards, or not being able to use for a long time. There...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A63B71/06G06N3/08G06N3/04G06K9/62G06K9/00
CPCA63B71/0619G06N3/08A63B2220/17A63B2220/806G06V20/41G06N3/045G06F18/214
Inventor 林峰鲁昱舟
Owner ZHEJIANG UNIV
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