Abstract
Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.
Highlights
Action recognition is one of the focus in current research studies
Many scholars have optimized and innovated the action recognition technology according to the characteristics of action changes and human body structure [1, 2]
The documents [3] optimized the action recognition method by using the improved deep convolution neural network and built a new recognition network by combining the Google Net network model with the idea of batch normalization transformation. e 4 documents [4] introduced the MEMS sensor network to collect the acceleration and angular velocity of gymnastics and recognized gymnastic movements by the classification model based on standard deviation, mean square error, and other classification feature of parameter setting, which has a high recognition rate
Summary
Action recognition is one of the focus in current research studies. Many scholars have optimized and innovated the action recognition technology according to the characteristics of action changes and human body structure [1, 2]. It is necessary to develop a new action recognition method of dance video image based on human-computer interaction technology. Substituting the calculation results of formulae (2)–(4) into the human skeleton model, the recognition of human skeleton motion features in the dance video images under the human-computer interaction technology [11, 12] can be realized. E model has the basic characteristics of bone angle change under formula (4), so it can recognize the characteristics of human bone movement in dance video images based on human-computer interaction technology [11, 12]. Under the control of human-computer interaction technology, the human skeleton model generates the posture according to the distance characteristics between the specific joint point and the central joint, among which 10 nodes were the most stable feature joint points. P(i) represents the 3D coordinate position vector of the joint point in the ith frame of the sequence; W(M) represents the set of vectors; and c(1), c(2), . . . , c(k) represents the extracted sample centers whose number is k. k cluster centroids are selected randomly and represented by o1, o2, . . . , oK, respectively, Right hand
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
