Abstract
With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video.
Highlights
With the continuous improvement of social informatization, more and more scenes are exposed to the lens, followed by the accumulation of a large number of images and video materials. ese materials contain rich data information, such as images and videos of human movement captured in different scenes and perspectives, with a large amount of data information of human skeleton detection. is information has broad application space in automatic driving, video retrieval, medical assistance, education, and teaching
In order to improve the detection speed and extraction accuracy of the model, this paper introduces the Particle swarm optimization (PSO)-enabled long short-term memory (LSTM) neural network and takes the dance video as the detection carrier to realize the detection and extraction of human skeleton based on videos [19]
In order to verify the effectiveness of PSO-LSTM neural network in human skeleton detection and extraction, the public MPII image data set [30] is selected and the individual data part containing different behavior patterns is labeled as the test data set
Summary
The detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. The existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Erefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Rough the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. E above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video
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