Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images Article Swipe
Srujana Gattupalli
,
Ashwin Ramesh Babu
,
James Brady
,
Fillia Makedon
,
Vassilis Athitsos
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1145/3197768.3201538
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1145/3197768.3201538
Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects. An application of hand keypoints detection is in performing cognitive assessments of a subject by observing the performance of that subject in physical tasks involving rapid finger motion. As a part of this work, we introduce a novel hand key-points benchmark dataset that consists of hand gestures recorded specifically for cognitive behavior monitoring. We explore the state of the art methods in hand keypoint detection and we provide quantitative evaluations for the performance of these methods on our dataset. In future, these results and our dataset can serve as a useful benchmark for hand keypoint recognition for rapid finger movements.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3197768.3201538
- OA Status
- green
- Cited By
- 1
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2796226609Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3197768.3201538Digital Object Identifier
- Title
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Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB ImagesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-06-26Full publication date if available
- Authors
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Srujana Gattupalli, Ashwin Ramesh Babu, James Brady, Fillia Makedon, Vassilis AthitsosList of authors in order
- Landing page
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https://doi.org/10.1145/3197768.3201538Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1804.01174Direct OA link when available
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Benchmark (surveying), Computer science, Artificial intelligence, RGB color model, Gesture, Computer vision, Key (lock), Deep learning, Pattern recognition (psychology), Machine learning, Geography, Computer security, GeodesyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.a | 34, 51, 58, 111 |
| abstract_inverted_index.An | 22 |
| abstract_inverted_index.As | 50 |
| abstract_inverted_index.In | 101 |
| abstract_inverted_index.We | 75 |
| abstract_inverted_index.an | 16 |
| abstract_inverted_index.as | 110 |
| abstract_inverted_index.by | 36 |
| abstract_inverted_index.in | 9, 19, 29, 43, 83 |
| abstract_inverted_index.is | 14, 28 |
| abstract_inverted_index.it | 13 |
| abstract_inverted_index.of | 24, 33, 40, 53, 66, 79, 95 |
| abstract_inverted_index.on | 98 |
| abstract_inverted_index.we | 56, 88 |
| abstract_inverted_index.and | 3, 87, 105 |
| abstract_inverted_index.art | 81 |
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| abstract_inverted_index.for | 71, 92, 114, 118 |
| abstract_inverted_index.has | 6 |
| abstract_inverted_index.our | 99, 106 |
| abstract_inverted_index.the | 38, 77, 80, 93 |
| abstract_inverted_index.Hand | 0 |
| abstract_inverted_index.hand | 25, 60, 67, 84, 115 |
| abstract_inverted_index.many | 20 |
| abstract_inverted_index.part | 52 |
| abstract_inverted_index.pose | 4 |
| abstract_inverted_index.that | 41, 64 |
| abstract_inverted_index.this | 54 |
| abstract_inverted_index.novel | 59 |
| abstract_inverted_index.rapid | 47, 119 |
| abstract_inverted_index.serve | 109 |
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| abstract_inverted_index.still | 15 |
| abstract_inverted_index.tasks | 45 |
| abstract_inverted_index.these | 96, 103 |
| abstract_inverted_index.work, | 55 |
| abstract_inverted_index.finger | 48, 120 |
| abstract_inverted_index.useful | 112 |
| abstract_inverted_index.dataset | 63, 107 |
| abstract_inverted_index.explore | 76 |
| abstract_inverted_index.future, | 102 |
| abstract_inverted_index.methods | 82, 97 |
| abstract_inverted_index.motion. | 49 |
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| abstract_inverted_index.results | 104 |
| abstract_inverted_index.subject | 35, 42 |
| abstract_inverted_index.vision, | 11 |
| abstract_inverted_index.aspects. | 21 |
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| abstract_inverted_index.computer | 10 |
| abstract_inverted_index.consists | 65 |
| abstract_inverted_index.dataset. | 100 |
| abstract_inverted_index.gestures | 68 |
| abstract_inverted_index.keypoint | 85, 116 |
| abstract_inverted_index.numerous | 7 |
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| abstract_inverted_index.recorded | 69 |
| abstract_inverted_index.unsolved | 17 |
| abstract_inverted_index.benchmark | 62, 113 |
| abstract_inverted_index.cognitive | 31, 72 |
| abstract_inverted_index.detection | 2, 27, 86 |
| abstract_inverted_index.introduce | 57 |
| abstract_inverted_index.involving | 46 |
| abstract_inverted_index.keypoints | 1, 26 |
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| abstract_inverted_index.estimation | 5 |
| abstract_inverted_index.key-points | 61 |
| abstract_inverted_index.movements. | 121 |
| abstract_inverted_index.performing | 30 |
| abstract_inverted_index.application | 23 |
| abstract_inverted_index.assessments | 32 |
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| abstract_inverted_index.monitoring. | 74 |
| abstract_inverted_index.performance | 39, 94 |
| abstract_inverted_index.recognition | 117 |
| abstract_inverted_index.applications | 8 |
| abstract_inverted_index.quantitative | 90 |
| abstract_inverted_index.specifically | 70 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.46775623 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |