Depth‐based end‐to‐end deep network for human action recognition Article Swipe
Sachin Chaudhary
,
Subrahmanyam Murala
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1049/iet-cvi.2018.5020
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1049/iet-cvi.2018.5020
Recognition of human actions from videos can be improved if depth information is available. Depth information certainly helps in segregating foreground motion from the background. Single image depth estimation (SIDE) is a commonly used method for the analysis of weather degraded images. In this study, the idea of SIDE is extended to human action recognition (HAR) on datasets where depth information is not available. Several depth‐based HAR algorithms are available but all of them are using the depth information given with the dataset. Some other methods are using depth motion map which refers to the depth of motion in a temporal direction. Here, a new depth‐based end‐to‐end deep network is proposed for HAR in which the frame‐wise depth is estimated and this estimated depth is used for processing instead of RGB frame. As colour information is not required for estimating motion, a single channel depth map is used for estimating motion in the video. It makes the system computationally efficient. The proposed method is tested and verified on three benchmark datasets namely JHMDB, HMDB51 and UCF101. The proposed method outperforms the existing state‐of‐the‐art methods for HAR on all the three tested datasets.
Related Topics
Concepts
Artificial intelligence
Computer science
RGB color model
Computer vision
Depth map
Benchmark (surveying)
Frame (networking)
Motion (physics)
End-to-end principle
Action recognition
Motion estimation
Pattern recognition (psychology)
Deep learning
Image (mathematics)
Class (philosophy)
Geography
Geodesy
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/iet-cvi.2018.5020
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-cvi.2018.5020
- OA Status
- bronze
- Cited By
- 29
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2892091048
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2892091048Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/iet-cvi.2018.5020Digital Object Identifier
- Title
-
Depth‐based end‐to‐end deep network for human action recognitionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-09-06Full publication date if available
- Authors
-
Sachin Chaudhary, Subrahmanyam MuralaList of authors in order
- Landing page
-
https://doi.org/10.1049/iet-cvi.2018.5020Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-cvi.2018.5020Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-cvi.2018.5020Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, RGB color model, Computer vision, Depth map, Benchmark (surveying), Frame (networking), Motion (physics), End-to-end principle, Action recognition, Motion estimation, Pattern recognition (psychology), Deep learning, Image (mathematics), Class (philosophy), Geography, Geodesy, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
29Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 2, 2022: 13, 2021: 4, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2626422042, https://openalex.org/W2689551733, https://openalex.org/W2034014085, https://openalex.org/W2126579184, https://openalex.org/W24089286, https://openalex.org/W2605111198, https://openalex.org/W2342662179, https://openalex.org/W1744759976, https://openalex.org/W2105101328, https://openalex.org/W1983364832, https://openalex.org/W2035669656, https://openalex.org/W2440315832, https://openalex.org/W2256362396, https://openalex.org/W2519481857, https://openalex.org/W2065101345, https://openalex.org/W2087864767, https://openalex.org/W2013076218, https://openalex.org/W2727485685, https://openalex.org/W2072070523, https://openalex.org/W2609621502, https://openalex.org/W2755624598, https://openalex.org/W2129619434, https://openalex.org/W1989665047, https://openalex.org/W2098782609, https://openalex.org/W2535410496, https://openalex.org/W2169852119, https://openalex.org/W410625161, https://openalex.org/W1893516992, https://openalex.org/W2964164518, https://openalex.org/W2019660985, https://openalex.org/W2963531836, https://openalex.org/W2462496837, https://openalex.org/W1522734439, https://openalex.org/W1944615693, https://openalex.org/W28988658, https://openalex.org/W764651262, https://openalex.org/W1966385142, https://openalex.org/W2963173190, https://openalex.org/W2128254161, https://openalex.org/W2156936307, https://openalex.org/W125693051, https://openalex.org/W2163605009, https://openalex.org/W2108598243, https://openalex.org/W1923332106, https://openalex.org/W2519080876, https://openalex.org/W2235034809, https://openalex.org/W2462996230, https://openalex.org/W2559655401 |
| referenced_works_count | 48 |
| abstract_inverted_index.a | 31, 99, 103, 141 |
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| abstract_inverted_index.The | 160, 176 |
| abstract_inverted_index.all | 71, 187 |
| abstract_inverted_index.and | 120, 165, 174 |
| abstract_inverted_index.are | 68, 74, 86 |
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| abstract_inverted_index.Here, | 102 |
| abstract_inverted_index.depth | 10, 27, 59, 77, 88, 95, 117, 123, 144 |
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| abstract_inverted_index.which | 91, 114 |
| abstract_inverted_index.(SIDE) | 29 |
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| abstract_inverted_index.JHMDB, | 172 |
| abstract_inverted_index.Single | 25 |
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| abstract_inverted_index.colour | 133 |
| abstract_inverted_index.frame. | 131 |
| abstract_inverted_index.method | 34, 162, 178 |
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| abstract_inverted_index.refers | 92 |
| abstract_inverted_index.single | 142 |
| abstract_inverted_index.study, | 44 |
| abstract_inverted_index.system | 157 |
| abstract_inverted_index.tested | 164, 190 |
| abstract_inverted_index.video. | 153 |
| abstract_inverted_index.videos | 5 |
| abstract_inverted_index.Several | 64 |
| abstract_inverted_index.UCF101. | 175 |
| abstract_inverted_index.actions | 3 |
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| abstract_inverted_index.images. | 41 |
| abstract_inverted_index.instead | 128 |
| abstract_inverted_index.methods | 85, 183 |
| abstract_inverted_index.motion, | 140 |
| abstract_inverted_index.network | 108 |
| abstract_inverted_index.weather | 39 |
| abstract_inverted_index.analysis | 37 |
| abstract_inverted_index.commonly | 32 |
| abstract_inverted_index.dataset. | 82 |
| abstract_inverted_index.datasets | 57, 170 |
| abstract_inverted_index.degraded | 40 |
| abstract_inverted_index.existing | 181 |
| abstract_inverted_index.extended | 50 |
| abstract_inverted_index.improved | 8 |
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| abstract_inverted_index.benchmark | 169 |
| abstract_inverted_index.certainly | 16 |
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| abstract_inverted_index.state‐of‐the‐art | 182 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5078635393 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I119241673 |
| citation_normalized_percentile.value | 0.84078731 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |