Movement Human Actions Recognition Based on Machine Learning Article Swipe
Honghua Xu
,
Li Li
,
Ming Fang
,
Fengrong Zhang
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3991/ijoe.v14i04.8513
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3991/ijoe.v14i04.8513
In this paper, the main technologies of foreground detection, feature description and extraction, movement behavior classification and recognition were introduced. Based on optical flow for movement objects detection, optical flow energy image was put forward for movement feature expression and region convolutional neural networks was adopt to choose features and decrease dimension. Then support vector machine classifier was trained and used to classify and recognize actions. After training and testing on public human actions database, the experiment result showed that the method could effectively distinguish human actions and significantly improved the recognition accuracy of human actions. And for the different situations of camera lens drawing near, pulling away or slight movement of camera, the solution had recognition effect as well. At last, this scheme was applied to intelligent video surveillance system, which was used to identify abnormal behavior and alarm. The abnormal behaviors of faint, smashing car, robbery and fighting were defined in the system. In running of the system, it obtained satisfactory recognition results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3991/ijoe.v14i04.8513
- https://online-journals.org/index.php/i-joe/article/download/8513/4924
- OA Status
- gold
- Cited By
- 32
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2801958295
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2801958295Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3991/ijoe.v14i04.8513Digital Object Identifier
- Title
-
Movement Human Actions Recognition Based on Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-04-26Full publication date if available
- Authors
-
Honghua Xu, Li Li, Ming Fang, Fengrong ZhangList of authors in order
- Landing page
-
https://doi.org/10.3991/ijoe.v14i04.8513Publisher landing page
- PDF URL
-
https://online-journals.org/index.php/i-joe/article/download/8513/4924Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://online-journals.org/index.php/i-joe/article/download/8513/4924Direct OA link when available
- Concepts
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Artificial intelligence, Optical flow, Computer science, Convolutional neural network, Classifier (UML), Computer vision, Feature extraction, Support vector machine, Movement (music), Pattern recognition (psychology), ALARM, Engineering, Image (mathematics), Philosophy, Aesthetics, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
32Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4, 2023: 7, 2022: 5, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.used | 60, 133 |
| abstract_inverted_index.were | 18, 150 |
| abstract_inverted_index.After | 66 |
| abstract_inverted_index.Based | 20 |
| abstract_inverted_index.adopt | 45 |
| abstract_inverted_index.could | 82 |
| abstract_inverted_index.human | 72, 85, 94 |
| abstract_inverted_index.image | 31 |
| abstract_inverted_index.last, | 121 |
| abstract_inverted_index.near, | 105 |
| abstract_inverted_index.video | 128 |
| abstract_inverted_index.well. | 119 |
| abstract_inverted_index.which | 131 |
| abstract_inverted_index.alarm. | 139 |
| abstract_inverted_index.camera | 102 |
| abstract_inverted_index.choose | 47 |
| abstract_inverted_index.effect | 117 |
| abstract_inverted_index.energy | 30 |
| abstract_inverted_index.faint, | 144 |
| abstract_inverted_index.method | 81 |
| abstract_inverted_index.neural | 42 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.public | 71 |
| abstract_inverted_index.region | 40 |
| abstract_inverted_index.result | 77 |
| abstract_inverted_index.scheme | 123 |
| abstract_inverted_index.showed | 78 |
| abstract_inverted_index.slight | 109 |
| abstract_inverted_index.vector | 54 |
| abstract_inverted_index.actions | 73, 86 |
| abstract_inverted_index.applied | 125 |
| abstract_inverted_index.camera, | 112 |
| abstract_inverted_index.defined | 151 |
| abstract_inverted_index.drawing | 104 |
| abstract_inverted_index.feature | 9, 37 |
| abstract_inverted_index.forward | 34 |
| abstract_inverted_index.machine | 55 |
| abstract_inverted_index.objects | 26 |
| abstract_inverted_index.optical | 22, 28 |
| abstract_inverted_index.pulling | 106 |
| abstract_inverted_index.robbery | 147 |
| abstract_inverted_index.running | 156 |
| abstract_inverted_index.support | 53 |
| abstract_inverted_index.system, | 130, 159 |
| abstract_inverted_index.system. | 154 |
| abstract_inverted_index.testing | 69 |
| abstract_inverted_index.trained | 58 |
| abstract_inverted_index.abnormal | 136, 141 |
| abstract_inverted_index.accuracy | 92 |
| abstract_inverted_index.actions. | 65, 95 |
| abstract_inverted_index.behavior | 14, 137 |
| abstract_inverted_index.classify | 62 |
| abstract_inverted_index.decrease | 50 |
| abstract_inverted_index.features | 48 |
| abstract_inverted_index.fighting | 149 |
| abstract_inverted_index.identify | 135 |
| abstract_inverted_index.improved | 89 |
| abstract_inverted_index.movement | 13, 25, 36, 110 |
| abstract_inverted_index.networks | 43 |
| abstract_inverted_index.obtained | 161 |
| abstract_inverted_index.results. | 164 |
| abstract_inverted_index.smashing | 145 |
| abstract_inverted_index.solution | 114 |
| abstract_inverted_index.training | 67 |
| abstract_inverted_index.behaviors | 142 |
| abstract_inverted_index.database, | 74 |
| abstract_inverted_index.different | 99 |
| abstract_inverted_index.recognize | 64 |
| abstract_inverted_index.classifier | 56 |
| abstract_inverted_index.detection, | 8, 27 |
| abstract_inverted_index.dimension. | 51 |
| abstract_inverted_index.experiment | 76 |
| abstract_inverted_index.expression | 38 |
| abstract_inverted_index.foreground | 7 |
| abstract_inverted_index.situations | 100 |
| abstract_inverted_index.description | 10 |
| abstract_inverted_index.distinguish | 84 |
| abstract_inverted_index.effectively | 83 |
| abstract_inverted_index.extraction, | 12 |
| abstract_inverted_index.intelligent | 127 |
| abstract_inverted_index.introduced. | 19 |
| abstract_inverted_index.recognition | 17, 91, 116, 163 |
| abstract_inverted_index.satisfactory | 162 |
| abstract_inverted_index.surveillance | 129 |
| abstract_inverted_index.technologies | 5 |
| abstract_inverted_index.convolutional | 41 |
| abstract_inverted_index.significantly | 88 |
| abstract_inverted_index.classification | 15 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.91510579 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |