| Title |
Extraction of Key Frames from Dance Videos and Movement Recognition by Multi-feature Fusion |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.6.495 |
| Keywords |
Multi-feature fusion; Dance videos; Key frame extraction; Movement recognition |
| Abstract |
Dance videos contain numerous complex movements that make movement recognition difficult. The multi-feature fusion method proposed in this paper first takes color and texture features of video frames for clustering, and then extracts key frames. Based on the key frames, spatial and temporal features, respectively, are extracted by using long short-term memory and a three-dimensional convolutional neural network. A multi-feature fusion method designed for movement recognition is used in experiments conducted with both the DanceDB dataset and a self-built dataset. The results show that the proposed multi-feature fusion method has high recall and precision ratios for key frame extraction, and achieved recognition accuracy of 42.67% and 50.64% with DanceDB and the self-built dataset, respectively. This paper validates the effectiveness of the proposed method for key frame extraction and movement recognition in dance videos, and suggests potential practical applications. The approach improves the reliability of video processing and provides theoretical support for further research on deep learning methods in the field of video processing. |