Object Detection in the Context of Mobile Augmented Reality Article Swipe
Xiang Li
,
Yuan Tian
,
Fuyao Zhang
,
Shuxue Quan
,
Yi Xu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2008.06655
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2008.06655
In the past few years, numerous Deep Neural Network (DNN) models and frameworks have been developed to tackle the problem of real-time object detection from RGB images. Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment. On the other hand, mobile Augmented Reality (AR) frameworks can continuously track a camera's pose within the scene and can estimate the correct scale of the environment by using Visual-Inertial Odometry (VIO). In this paper, we propose a novel approach that combines the geometric information from VIO with semantic information from object detectors to improve the performance of object detection on mobile devices. Our approach includes three components: (1) an image orientation correction method, (2) a scale-based filtering approach, and (3) an online semantic map. Each component takes advantage of the different characteristics of the VIO-based AR framework. We implemented the AR-enhanced features using ARCore and the SSD Mobilenet model on Android phones. To validate our approach, we manually labeled objects in image sequences taken from 12 room-scale AR sessions. The results show that our approach can improve on the accuracy of generic object detectors by 12% on our dataset.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2008.06655
- https://arxiv.org/pdf/2008.06655
- OA Status
- green
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3048990554
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3048990554Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2008.06655Digital Object Identifier
- Title
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Object Detection in the Context of Mobile Augmented RealityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-15Full publication date if available
- Authors
-
Xiang Li, Yuan Tian, Fuyao Zhang, Shuxue Quan, Yi XuList of authors in order
- Landing page
-
https://arxiv.org/abs/2008.06655Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2008.06655Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2008.06655Direct OA link when available
- Concepts
-
Computer science, Augmented reality, Artificial intelligence, Computer vision, Object detection, Object (grammar), Pose, Odometry, Mobile device, RGB color model, Scale (ratio), Android (operating system), Mobile robot, Pattern recognition (psychology), Robot, Quantum mechanics, Operating system, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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45Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 11, 37, 50, 74, 135, 161 |
| abstract_inverted_index.are | 39 |
| abstract_inverted_index.can | 65, 75, 192 |
| abstract_inverted_index.few | 3 |
| abstract_inverted_index.our | 171, 190, 204 |
| abstract_inverted_index.the | 1, 18, 34, 42, 48, 51, 54, 57, 72, 77, 81, 98, 111, 146, 150, 156, 162, 195 |
| abstract_inverted_index.(AR) | 63 |
| abstract_inverted_index.Deep | 6 |
| abstract_inverted_index.Each | 141 |
| abstract_inverted_index.been | 14 |
| abstract_inverted_index.from | 24, 33, 101, 106, 181 |
| abstract_inverted_index.have | 13 |
| abstract_inverted_index.map. | 140 |
| abstract_inverted_index.past | 2 |
| abstract_inverted_index.pose | 44, 70 |
| abstract_inverted_index.show | 188 |
| abstract_inverted_index.that | 96, 189 |
| abstract_inverted_index.they | 38 |
| abstract_inverted_index.this | 89 |
| abstract_inverted_index.with | 45, 103 |
| abstract_inverted_index.(DNN) | 9 |
| abstract_inverted_index.hand, | 59 |
| abstract_inverted_index.image | 126, 178 |
| abstract_inverted_index.model | 165 |
| abstract_inverted_index.novel | 94 |
| abstract_inverted_index.only, | 36 |
| abstract_inverted_index.other | 58 |
| abstract_inverted_index.scale | 52, 79 |
| abstract_inverted_index.scene | 73 |
| abstract_inverted_index.taken | 180 |
| abstract_inverted_index.takes | 143 |
| abstract_inverted_index.three | 122 |
| abstract_inverted_index.track | 67 |
| abstract_inverted_index.using | 84, 159 |
| abstract_inverted_index.(VIO). | 87 |
| abstract_inverted_index.ARCore | 160 |
| abstract_inverted_index.Neural | 7 |
| abstract_inverted_index.camera | 43 |
| abstract_inverted_index.images | 35 |
| abstract_inverted_index.mobile | 60, 117 |
| abstract_inverted_index.models | 10 |
| abstract_inverted_index.object | 22, 28, 107, 114, 199 |
| abstract_inverted_index.online | 138 |
| abstract_inverted_index.paper, | 90 |
| abstract_inverted_index.regard | 46 |
| abstract_inverted_index.tackle | 17 |
| abstract_inverted_index.within | 71 |
| abstract_inverted_index.years, | 4 |
| abstract_inverted_index.Android | 167 |
| abstract_inverted_index.Network | 8 |
| abstract_inverted_index.Reality | 62 |
| abstract_inverted_index.correct | 78 |
| abstract_inverted_index.generic | 198 |
| abstract_inverted_index.images. | 26 |
| abstract_inverted_index.improve | 110, 193 |
| abstract_inverted_index.labeled | 175 |
| abstract_inverted_index.method, | 129 |
| abstract_inverted_index.objects | 176 |
| abstract_inverted_index.phones. | 168 |
| abstract_inverted_index.problem | 19 |
| abstract_inverted_index.process | 31 |
| abstract_inverted_index.propose | 92 |
| abstract_inverted_index.results | 187 |
| abstract_inverted_index.Odometry | 86 |
| abstract_inverted_index.Ordinary | 27 |
| abstract_inverted_index.accuracy | 196 |
| abstract_inverted_index.approach | 95, 120, 191 |
| abstract_inverted_index.camera's | 69 |
| abstract_inverted_index.combines | 97 |
| abstract_inverted_index.dataset. | 205 |
| abstract_inverted_index.devices. | 118 |
| abstract_inverted_index.estimate | 76 |
| abstract_inverted_index.features | 158 |
| abstract_inverted_index.includes | 121 |
| abstract_inverted_index.manually | 174 |
| abstract_inverted_index.numerous | 5 |
| abstract_inverted_index.semantic | 104, 139 |
| abstract_inverted_index.validate | 170 |
| abstract_inverted_index.Augmented | 61 |
| abstract_inverted_index.Mobilenet | 164 |
| abstract_inverted_index.VIO-based | 151 |
| abstract_inverted_index.advantage | 144 |
| abstract_inverted_index.approach, | 134, 172 |
| abstract_inverted_index.component | 142 |
| abstract_inverted_index.detection | 23, 29, 115 |
| abstract_inverted_index.detectors | 108, 200 |
| abstract_inverted_index.developed | 15 |
| abstract_inverted_index.different | 147 |
| abstract_inverted_index.filtering | 133 |
| abstract_inverted_index.geometric | 99 |
| abstract_inverted_index.oblivious | 40 |
| abstract_inverted_index.real-time | 21 |
| abstract_inverted_index.sequences | 179 |
| abstract_inverted_index.sessions. | 185 |
| abstract_inverted_index.approaches | 30 |
| abstract_inverted_index.correction | 128 |
| abstract_inverted_index.framework. | 153 |
| abstract_inverted_index.frameworks | 12, 64 |
| abstract_inverted_index.room-scale | 183 |
| abstract_inverted_index.AR-enhanced | 157 |
| abstract_inverted_index.components: | 123 |
| abstract_inverted_index.environment | 49, 82 |
| abstract_inverted_index.implemented | 155 |
| abstract_inverted_index.information | 32, 100, 105 |
| abstract_inverted_index.orientation | 127 |
| abstract_inverted_index.performance | 112 |
| abstract_inverted_index.scale-based | 132 |
| abstract_inverted_index.continuously | 66 |
| abstract_inverted_index.environment. | 55 |
| abstract_inverted_index.Visual-Inertial | 85 |
| abstract_inverted_index.characteristics | 148 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |