High-Dynamic Dance Motion Recognition Method Based on Video Visual Analysis

Abstract

In the field of computer vision, high-dynamic dance motion recognition is a difficult problem to solve. Its goal is to recognize human motion by analyzing video data using image processing and classification recognition technology. Video multifeature fusion has sparked a surge in research in a variety of fields. Several pixel points that can be distinguished and displayed in several adjacent images that can reflect their characteristics are referred to as multifeature fusion. It is responsible for a significant portion of the similarity results between the two video segments. Motion recognition relies heavily on video multifeature fusion, which has a direct impact on the robustness and accuracy of recognition results. The directional gradient histogram features, optical flow direction histogram features, and audio features extracted from dance video are used to characterize dance movements after all of the characteristics of dance movements have been considered. This paper focuses on the high-dynamic dance action recognition method based on video multifeature fusion, which aims to combine high-dynamic dance action recognition and video multifeature fusion.

An Intelligent Mobile Application to Analyze Dance Movements and Provide Feedbacks using Media Pipe and OpenCV

“…This method uses multi-feature fusion, where the extracted features are combined to get a comprehensive representation of dance actions. According to the source, this system enhances the precision in information retrieval [4]. Some of the limitations are high dimensionality and redundancy of the fusion method, and the difficulty in accurately extracting key frames from dance videos.…”

A Deep Learning Algorithm for Fusing Action Recognition and Psychological Characteristics of Wrestlers

“…The impact of different environments on person recognition is shown in Fig. 2 [12][13]. In wrestling, these influencing factors exist widely, so a more in-depth calculation method should be used for their identification.…”

“…This paper adopts a human body identification method based on bone positioning. The first branch takes the overall skeleton point sequence [24,25,12,11,10,9,21,5,6,8,7,8,22,23,4,3,21,2,1,17,18,19,20,13,14,15,16] are input to a two-layer LSTM network, and the second layer of LSTM extracts the entire frame information. The second branch divides the body skeleton point sequence into the left branch [24,25,12,11,10,9,17,18,19,20], the torso [1,2,3,21,4] and the right branch [22,23,8,7,6,5,13,14,15,…”

“…The double-layer LSTM network is input, and the second layer of LSTM accepts the output of the first layer and processes it, making the judgment of actions more accurate. The third branch divides the body skeleton point sequence into the left arm [5,6,7,8,22,23], the right arm [9,10,11,12,24,25], the left leg [13,14,15,16], right leg [17,18,19,20] and torso [1,2,3,21,4], this branch temporal model is similar to the local temporal model to divide the human skeleton into more parts in order to better identify local detailed actions. It can effectively improve the accuracy of detailed action recognition.…”

“…c) The output layer is calculated through the loss function . d) For l=L-1 to 2, backpropagation is calculated according to the current layer type: If it is currently a fully connected layer: (12) If it is currently a fully convolutional layer: (13) If the current full pooling layer: (14) For l = 2 to L, update the first layer according to the type of the current layer , : If it is currently a fully connected layer: (15) (16) If it is currently a convolutional layer, for each convolution kernel there are: (17) (18) Iterating will stop when all parameter changes are less than .…”