Dancer Tracking Algorithm in Ethnic Areas Based on Multifeature Fusion Neural Network

• Published in: Wireless communications and mobile computing Vol. 2022; no. 1
• Main Author: Shi, Yuzhu
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Contours; Dance; Dancers & choreographers; Distance learning; Error reduction; Feature extraction; Feature recognition; Image enhancement; Methods; Motion perception; Neural networks; Tracking; Visual perception
• ISSN: 1530-8669; 1530-8677; 1530-8677
• DOI: 10.1155/2022/4796937

Due to the complex posture changes in dance movements, accurate detection and tracking of human targets are carried out in order to improve the guidance ability of dancers in ethnic areas. A multifeature fusion-based tracking algorithm for dancers in ethnic areas is proposed. The edge contour model of video images of dancers in ethnic areas is detected, and the video tracking scanning imaging model of dancers in ethnic areas is constructed. The video images of dancers in ethnic areas are enhanced based on the initial contour distribution, and a visual perception model of dancers tracking images in ethnic areas is established. To improve the algorithm’s estimation of complex poses and finally complete the dance movement recognition, a feature pyramid network is used to extract the features of dance movements, and then, a multifeature fusion module is used to fuse multiple features. The tracking algorithm proposed in this paper has higher robustness than other algorithms and effectively reduces the error samples generated during the tracking process, thus improving the accuracy of long-term tracking.