A key frame supervised deep learning framework of dance boundary detection and movement identification

• Published in: 2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST) pp. 649 - 655
• Main Authors: Wei, Ruicheng, Zuo, Min, Yan, Wenjing, Zhang, Qingchuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.12.2023
• Subjects: Action recognition; Annotations; component; Dance movements; Deep learning; Dual-flow network; Feature fusion; Humanities; Knowledge engineering; Neural networks; Supervised learning; Technological innovation; Timing model
• DOI: 10.1109/IAECST60924.2023.10502543

Accurate boundary detection with the start and end of movement and its identification in video recognition have always been a challenging subject, especially in dance movement detection issue due the high complexity. This paper proposes a deep learning framework to perform effective feature extraction and movement evaluation. First, we constructed the dataset collecting 5 dance movements of modern dance, and we introduced expert knowledge to construct the annotations for the action keyframe dataset. Secondly, we utilize a dual-stream network algorithm to capture the features of movements in dance videos. Subsequently, we introduced key keyframes in constructing the start, the keyframe and the end of dance movement, which could enhance the supervision. Finally, the movement identification module was fulfilled by combining extracted action feature and keyframe features. The experimental results achieved a maximum recall of 89% in the boundary segmentation of dance videos, and achieved a maximum accuracy of 65% in dance movement identification. The results showed that the framework can realize highly accurate dance movement recognition and boundary recognition, which provides strong support for artistic creation, teaching and performance in the field of dance.