Abstract
Video analysis of human motion has been widely used in intelligent monitoring, sports analysis, and virtual reality as a research hotspot in computer vision. It is necessary to decompose and track the movements in the process of movement in order to improve the training quality in dance training. The traditional motion tracking decomposition method, on the other hand, is unable to calculate the visual changes of adjacent key nodes, and the contour of 3D visual motion tracking remains ambiguous. This paper applies the human posture estimation algorithm in computer vision to the detection of key points of rectangular objects and obtains the heat map of key points of rectangular objects by adding a lightweight feature extraction network and a feature pyramid layer integrating multilayer semantic information, on the basis of summarizing and analyzing related research work at home and abroad. Because of the fusion of multilayer information, the network’s design not only reduces the amount of calculation and parameters but also improves the accuracy of the final detection result. The test results show that the proposed algorithm’s recognition accuracy has improved.
Highlights
Motion recognition is a very challenging topic in the field of computer vision, and it has become a very popular research direction in recent years because of its high research value [1]
When studying the real-time tracking information of dance movements and postures, it can help to identify subtle contour changes in dance movements. e traditional realtime tracking method of dance posture based on the recognition rate is not high, and it is unable to identify the small changes in dance movements
The dance dynamic recognition method based on this lightweight network can keep the recognition accuracy and better recognition effect with the increasing of recognition parameters, which is of developmental significance for real-time tracking of dance posture
Summary
Motion recognition is a very challenging topic in the field of computer vision, and it has become a very popular research direction in recent years because of its high research value [1]. E traditional basic dance training adopts the uncalibrated global visual feedback method, that is, the traditional video decomposition training. Traditional dance pose estimation and key point detection techniques mostly rely on complex image processing techniques and postprocessing skills, and the inference speed and accuracy are low. With the rapid development of deep learning (DL) and computer vision [3,4], lightweight network has gradually become the main development direction of attitude estimation and key point detection, which has more accurate results and more possibilities than traditional methods. The research on the combination of action recognition technology and dance action is still in its infancy. Due to the high complexity of dance action and the problems of human self-shielding when performing dance, the research progress in dance video action recognition is relatively slow
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