Martial Arts Moves Recognition Method Based on Visual Image
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초록
Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few exampleswhere visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote anddevelop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes thedenoising of the original data using the wavelet transform and provides a sliding window data segmentationtechnique. Wushu movement The Wushu movement recognition model is built based on the hidden Markovmodel (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which isthen enhanced using the frequency weighted training approach and the mean training method. To identify thedynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal statesequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movementsrecognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when thenumber of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%),and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martialarts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.
Abstract
Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few exampleswhere visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote anddevelop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes thedenoising of the original data using the wavelet transform and provides a sliding window data segmentationtechnique. Wushu movement The Wushu movement recognition model is built based on the hidden Markovmodel (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which isthen enhanced using the frequency weighted training approach and the mean training method. To identify thedynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal statesequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movementsrecognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when thenumber of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%),and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martialarts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.
참고문헌 (17)
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