ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs Article Swipe
Niluthpol Chowdhury Mithun
,
Sirajum Munir
,
Karen Guo
,
Charles Shelton
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/ipsn.2018.00051
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/ipsn.2018.00051
Detecting objects that are carried when someone enters or exits a room is very useful for a wide range of smart building applications including safety, security, and energy efficiency. While there has been a significant amount of work on object recognition using large-scale RGB image datasets, RGB cameras are too privacy invasive in many smart building applications and they work poorly in the dark. Additionally, deep object detection networks require powerful and expensive GPUs. We propose a novel system that we call ODDS (Object Detector using a Depth Sensor) that can detect objects in real-time using only raw depth data on an embedded GPU, e.g., NVIDIA Jetson TX1. Hence, our solution is significantly less privacy invasive (even if the sensor is compromised) and less expensive, while maintaining a comparable accuracy with state of the art solutions. Specifically, we resort to training a deep convolutional neural network using raw depth images, with curriculum based learning to improve accuracy by considering the complexity and imbalance in object classes and developing a sparse coding based technique that speeds up the system ~2× with minimal loss of accuracy. Based on a complete implementation and real-world evaluation, we see ODDS achieve 80.14% mean average precision in object detection in real-time (5-6 FPS) on a Jetson TX1.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ipsn.2018.00051
- OA Status
- green
- Cited By
- 14
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2798936443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2798936443Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ipsn.2018.00051Digital Object Identifier
- Title
-
ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-04-01Full publication date if available
- Authors
-
Niluthpol Chowdhury Mithun, Sirajum Munir, Karen Guo, Charles SheltonList of authors in order
- Landing page
-
https://doi.org/10.1109/ipsn.2018.00051Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/1811681Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Object detection, RGB color model, Deep learning, Artificial intelligence, Cognitive neuroscience of visual object recognition, Object (grammar), Computer vision, Real-time computing, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 3, 2022: 1, 2021: 3, 2019: 6Per-year citation counts (last 5 years)
- References (count)
-
73Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.RGB | 43, 46 |
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| abstract_inverted_index.e.g., | 104 |
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| abstract_inverted_index.NVIDIA | 105 |
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| abstract_inverted_index.speeds | 174 |
| abstract_inverted_index.system | 78, 177 |
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| abstract_inverted_index.average | 198 |
| abstract_inverted_index.cameras | 47 |
| abstract_inverted_index.carried | 4 |
| abstract_inverted_index.classes | 165 |
| abstract_inverted_index.images, | 149 |
| abstract_inverted_index.improve | 155 |
| abstract_inverted_index.minimal | 180 |
| abstract_inverted_index.network | 145 |
| abstract_inverted_index.objects | 1, 92 |
| abstract_inverted_index.privacy | 50, 114 |
| abstract_inverted_index.propose | 75 |
| abstract_inverted_index.require | 69 |
| abstract_inverted_index.safety, | 24 |
| abstract_inverted_index.someone | 6 |
| abstract_inverted_index.Detector | 84 |
| abstract_inverted_index.accuracy | 129, 156 |
| abstract_inverted_index.building | 21, 55 |
| abstract_inverted_index.complete | 187 |
| abstract_inverted_index.embedded | 102 |
| abstract_inverted_index.invasive | 51, 115 |
| abstract_inverted_index.learning | 153 |
| abstract_inverted_index.networks | 68 |
| abstract_inverted_index.powerful | 70 |
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| abstract_inverted_index.training | 140 |
| abstract_inverted_index.Detecting | 0 |
| abstract_inverted_index.accuracy. | 183 |
| abstract_inverted_index.datasets, | 45 |
| abstract_inverted_index.detection | 67, 202 |
| abstract_inverted_index.expensive | 72 |
| abstract_inverted_index.imbalance | 162 |
| abstract_inverted_index.including | 23 |
| abstract_inverted_index.precision | 199 |
| abstract_inverted_index.real-time | 94, 204 |
| abstract_inverted_index.security, | 25 |
| abstract_inverted_index.technique | 172 |
| abstract_inverted_index.comparable | 128 |
| abstract_inverted_index.complexity | 160 |
| abstract_inverted_index.curriculum | 151 |
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| abstract_inverted_index.real-world | 190 |
| abstract_inverted_index.solutions. | 135 |
| abstract_inverted_index.considering | 158 |
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| abstract_inverted_index.evaluation, | 191 |
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| abstract_inverted_index.recognition | 40 |
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| abstract_inverted_index.Additionally, | 64 |
| abstract_inverted_index.Specifically, | 136 |
| abstract_inverted_index.convolutional | 143 |
| abstract_inverted_index.significantly | 112 |
| abstract_inverted_index.implementation | 188 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.80555998 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |