The Role of the Combination of 3D Simulation Sequence Diagram and Video Motion Recognition Technology in Evaluating and Correcting Dancers' Dance Moves Article Swipe
This paper combines the dance 3D space simulation sequence diagram with video motion recognition technology, filters, denoises, grays and background removal the collected dance video images, analyzes the motion characteristics of people in the sequence diagram, uses support vector machine to learn and train 3D space models, classifies and recognizes people's dance movements, and extracts 3D-SIFT and optical flow characteristics of various areas of human body. Form a three-dimensional space simulation sequence diagram, reduce and normalize the extracted features, get the feature vectors of various characters, and input them into the classifier to realize the recognition of dance movements. The results show that the combination of 3D-SIFT and optical flow can realize the dynamic change of human static information, the illumination invariance of SIFT features can make up for the illumination sensitivity of optical flow features, and the optical flow features can solve the instability problem of determining the key points of SIFT features.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4018/ijmcmc.348662
- https://www.igi-global.com/ViewTitle.aspx?TitleId=348662&isxn=9798369324745
- OA Status
- diamond
- Cited By
- 1
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400799033
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400799033Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4018/ijmcmc.348662Digital Object Identifier
- Title
-
The Role of the Combination of 3D Simulation Sequence Diagram and Video Motion Recognition Technology in Evaluating and Correcting Dancers' Dance MovesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-17Full publication date if available
- Authors
-
Wei Hua, Vinh ChauList of authors in order
- Landing page
-
https://doi.org/10.4018/ijmcmc.348662Publisher landing page
- PDF URL
-
https://www.igi-global.com/ViewTitle.aspx?TitleId=348662&isxn=9798369324745Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.igi-global.com/ViewTitle.aspx?TitleId=348662&isxn=9798369324745Direct OA link when available
- Concepts
-
Scale-invariant feature transform, Computer vision, Artificial intelligence, Computer science, Optical flow, Dance, Sequence (biology), Diagram, Feature vector, Motion (physics), Classifier (UML), Motion analysis, Feature extraction, Image (mathematics), Genetics, Art, Biology, Database, LiteratureTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.realize | 93, 111 |
| abstract_inverted_index.removal | 20 |
| abstract_inverted_index.results | 100 |
| abstract_inverted_index.support | 37 |
| abstract_inverted_index.various | 61, 84 |
| abstract_inverted_index.vectors | 82 |
| abstract_inverted_index.analyzes | 26 |
| abstract_inverted_index.combines | 2 |
| abstract_inverted_index.diagram, | 35, 72 |
| abstract_inverted_index.extracts | 54 |
| abstract_inverted_index.features | 124, 140 |
| abstract_inverted_index.filters, | 15 |
| abstract_inverted_index.people's | 50 |
| abstract_inverted_index.sequence | 8, 34, 71 |
| abstract_inverted_index.collected | 22 |
| abstract_inverted_index.denoises, | 16 |
| abstract_inverted_index.extracted | 77 |
| abstract_inverted_index.features, | 78, 135 |
| abstract_inverted_index.features. | 153 |
| abstract_inverted_index.normalize | 75 |
| abstract_inverted_index.background | 19 |
| abstract_inverted_index.classifier | 91 |
| abstract_inverted_index.classifies | 47 |
| abstract_inverted_index.invariance | 121 |
| abstract_inverted_index.movements, | 52 |
| abstract_inverted_index.movements. | 98 |
| abstract_inverted_index.recognizes | 49 |
| abstract_inverted_index.simulation | 7, 70 |
| abstract_inverted_index.characters, | 85 |
| abstract_inverted_index.combination | 104 |
| abstract_inverted_index.determining | 147 |
| abstract_inverted_index.instability | 144 |
| abstract_inverted_index.recognition | 13, 95 |
| abstract_inverted_index.sensitivity | 131 |
| abstract_inverted_index.technology, | 14 |
| abstract_inverted_index.illumination | 120, 130 |
| abstract_inverted_index.information, | 118 |
| abstract_inverted_index.characteristics | 29, 59 |
| abstract_inverted_index.three-dimensional | 68 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.57443771 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |