Changes
On May 10, 2022 at 4:54:18 PM CDT, Yanique Campbell:
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Removed tag beluga from Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning
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Added tag Beluga to Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning
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Changed value of field
related_datasets
to[]
in Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning -
Changed value of field
keywords
toUnmanned Aerial Vehicle,computer vision,deep learning,object detection,object tracking,Beluga
in Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning
f | 1 | { | f | 1 | { |
2 | "Author": [ | 2 | "Author": [ | ||
3 | { | 3 | { | ||
4 | "affiliation": "Centre for Earth Observation Science - | 4 | "affiliation": "Centre for Earth Observation Science - | ||
5 | University of Manitoba", | 5 | University of Manitoba", | ||
6 | "creatorName": "Harasyn, Madison", | 6 | "creatorName": "Harasyn, Madison", | ||
7 | "email": "Madison.harasyn@umanitoba.ca", | 7 | "email": "Madison.harasyn@umanitoba.ca", | ||
8 | "nameIdentifier": "https://orcid.org/0000-0002-5741-6766", | 8 | "nameIdentifier": "https://orcid.org/0000-0002-5741-6766", | ||
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12 | }, | 12 | }, | ||
13 | { | 13 | { | ||
14 | "affiliation": "Centre for Earth Observation Science - | 14 | "affiliation": "Centre for Earth Observation Science - | ||
15 | University of Manitoba", | 15 | University of Manitoba", | ||
16 | "creatorName": "Chan, Wayne", | 16 | "creatorName": "Chan, Wayne", | ||
17 | "email": "wayne.chan@umanitoba.ca", | 17 | "email": "wayne.chan@umanitoba.ca", | ||
18 | "nameIdentifier": "", | 18 | "nameIdentifier": "", | ||
19 | "nameType": "Personal" | 19 | "nameType": "Personal" | ||
20 | }, | 20 | }, | ||
21 | { | 21 | { | ||
22 | "affiliation": "Centre for Earth Observation Science - | 22 | "affiliation": "Centre for Earth Observation Science - | ||
23 | University of Manitoba", | 23 | University of Manitoba", | ||
24 | "creatorName": "Ausen, Emma", | 24 | "creatorName": "Ausen, Emma", | ||
25 | "email": "emma.ausen@umanitoba.ca", | 25 | "email": "emma.ausen@umanitoba.ca", | ||
26 | "nameIdentifier": "", | 26 | "nameIdentifier": "", | ||
27 | "nameType": "Personal" | 27 | "nameType": "Personal" | ||
28 | }, | 28 | }, | ||
29 | { | 29 | { | ||
30 | "affiliation": "Centre for Earth Observation Science - | 30 | "affiliation": "Centre for Earth Observation Science - | ||
31 | University of Manitoba", | 31 | University of Manitoba", | ||
32 | "creatorName": "Barber, David", | 32 | "creatorName": "Barber, David", | ||
33 | "email": "david.barber@umanitoba.ca", | 33 | "email": "david.barber@umanitoba.ca", | ||
34 | "nameIdentifier": "0000-0001-9466-3291", | 34 | "nameIdentifier": "0000-0001-9466-3291", | ||
35 | "nameIdentifierScheme": "ORCID", | 35 | "nameIdentifierScheme": "ORCID", | ||
36 | "nameType": "Personal", | 36 | "nameType": "Personal", | ||
37 | "schemeURI": "http://orcid.org/" | 37 | "schemeURI": "http://orcid.org/" | ||
38 | } | 38 | } | ||
39 | ], | 39 | ], | ||
40 | "Identifier": "10.1139/juvs-2021-0024", | 40 | "Identifier": "10.1139/juvs-2021-0024", | ||
41 | "PublicationYear": "2022", | 41 | "PublicationYear": "2022", | ||
42 | "Publisher": "Drone Systems and Applications", | 42 | "Publisher": "Drone Systems and Applications", | ||
43 | "ResourceType": "journal article", | 43 | "ResourceType": "journal article", | ||
44 | "Rights": "Creative Commons Attribution 4.0 International", | 44 | "Rights": "Creative Commons Attribution 4.0 International", | ||
45 | "Version": "1.0", | 45 | "Version": "1.0", | ||
46 | "author": null, | 46 | "author": null, | ||
47 | "author_email": null, | 47 | "author_email": null, | ||
48 | "awardTitle": "The Canada Excellence Research Chair (CERC) and the | 48 | "awardTitle": "The Canada Excellence Research Chair (CERC) and the | ||
49 | Canada Research Chair (CRC programs)", | 49 | Canada Research Chair (CRC programs)", | ||
50 | "awardURI": "https://www.cerc.gc.ca/", | 50 | "awardURI": "https://www.cerc.gc.ca/", | ||
51 | "citation": "Madison L.Harasyn, Wayne S.Chan, Emma L.Ausen, and | 51 | "citation": "Madison L.Harasyn, Wayne S.Chan, Emma L.Ausen, and | ||
52 | David G.Barber. Detection and tracking of belugas, kayaks and | 52 | David G.Barber. Detection and tracking of belugas, kayaks and | ||
53 | motorized boats in drone video using deep learning. Drone Systems and | 53 | motorized boats in drone video using deep learning. Drone Systems and | ||
54 | Applications. 10(1): 77-96. https://doi.org/10.1139/juvs-2021-0024", | 54 | Applications. 10(1): 77-96. https://doi.org/10.1139/juvs-2021-0024", | ||
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56 | "descriptionType": "Abstract", | 56 | "descriptionType": "Abstract", | ||
57 | "funderIdentifier": "", | 57 | "funderIdentifier": "", | ||
58 | "funderIdentifierType": "", | 58 | "funderIdentifierType": "", | ||
59 | "funderName": "", | 59 | "funderName": "", | ||
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62 | "groups": [ | 62 | "groups": [ | ||
63 | { | 63 | { | ||
n | 64 | "description": "Inland water features, drainage systems and | n | 64 | "description": "Image: \"Earth from Space\" by NASA Goddard |
65 | their characteristics. Examples of data you can find here include | 65 | Photo and Video is licensed under CC BY 2.0", | ||
66 | river and lake data, water quality data. \r\n\r\nIn CEOS, related | ||||
67 | research themes include biogeochemistry, Inland lakes and waters, | ||||
68 | modelling, remote sensing and technology, trace metals and | ||||
69 | contaminants.", | ||||
70 | "display_name": "Freshwater", | 66 | "display_name": "Remote Sensing", | ||
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n | 73 | /data/uploads/group/2021-10-31-211937.658599hyinspirehydrography.svg", | n | 69 | anitoba.ca/data/uploads/group/2022-02-05-222621.346712earthimage.jpg", |
74 | "name": "freshwater", | 70 | "name": "remote-sensing", | ||
75 | "title": "Freshwater" | 71 | "title": "Remote Sensing" | ||
76 | }, | ||||
77 | { | ||||
78 | "description": "Features and characteristics of salt water | ||||
79 | bodies.\r\n\r\nIn CEOS, related research themes include | ||||
80 | biogeochemistry, modelling, marine mammals, oil spill response, | ||||
81 | physical oceanography, remote sensing and technology and trace metals | ||||
82 | and contaminants", | ||||
83 | "display_name": "Marine", | ||||
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87 | "name": "marine", | ||||
88 | "title": "Marine" | ||||
89 | } | 72 | } | ||
90 | ], | 73 | ], | ||
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92 | "isopen": false, | 75 | "isopen": false, | ||
n | 93 | "keywords": "Unmanned Aerial Vehicle,beluga,computer vision,deep | n | 76 | "keywords": "Unmanned Aerial Vehicle,computer vision,deep |
94 | learning,object detection,object tracking", | 77 | learning,object detection,object tracking,Beluga", | ||
95 | "language": "English", | 78 | "language": "English", | ||
96 | "licenceType": "Open", | 79 | "licenceType": "Open", | ||
97 | "license_id": null, | 80 | "license_id": null, | ||
98 | "license_title": null, | 81 | "license_title": null, | ||
99 | "maintainer": null, | 82 | "maintainer": null, | ||
100 | "maintainer_email": null, | 83 | "maintainer_email": null, | ||
101 | "metadata_created": "2022-04-07T19:45:13.021227", | 84 | "metadata_created": "2022-04-07T19:45:13.021227", | ||
n | 102 | "metadata_modified": "2022-04-07T20:02:20.616080", | n | 85 | "metadata_modified": "2022-05-10T21:54:18.642115", |
103 | "name": | 86 | "name": | ||
104 | elugas-kayaks-and-motorized-boats-in-drone-video-using-deep-learning", | 87 | elugas-kayaks-and-motorized-boats-in-drone-video-using-deep-learning", | ||
105 | "notes": "Aerial imagery surveys are commonly used in marine mammal | 88 | "notes": "Aerial imagery surveys are commonly used in marine mammal | ||
106 | research to determine population size, distribution and habitat use. | 89 | research to determine population size, distribution and habitat use. | ||
107 | Analysis of aerial photos involves hours of manually identifying | 90 | Analysis of aerial photos involves hours of manually identifying | ||
108 | individuals present in each image and converting raw counts into | 91 | individuals present in each image and converting raw counts into | ||
109 | useable biological statistics. Our research proposes the use of deep | 92 | useable biological statistics. Our research proposes the use of deep | ||
110 | learning algorithms to increase the efficiency of the marine mammal | 93 | learning algorithms to increase the efficiency of the marine mammal | ||
111 | research workflow. To test the feasibility of this proposal, the | 94 | research workflow. To test the feasibility of this proposal, the | ||
112 | existing YOLOv4 convolutional neural network model was trained to | 95 | existing YOLOv4 convolutional neural network model was trained to | ||
113 | detect belugas, kayaks and motorized boats in oblique drone imagery, | 96 | detect belugas, kayaks and motorized boats in oblique drone imagery, | ||
114 | collected from a stationary tethered system. Automated computer-based | 97 | collected from a stationary tethered system. Automated computer-based | ||
115 | object detection achieved the following precision and recall, | 98 | object detection achieved the following precision and recall, | ||
116 | respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and | 99 | respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and | ||
117 | kayak = 96%/96%. We then tested the performance of computer vision | 100 | kayak = 96%/96%. We then tested the performance of computer vision | ||
118 | tracking of belugas and occupied watercraft in drone videos using the | 101 | tracking of belugas and occupied watercraft in drone videos using the | ||
119 | DeepSORT tracking algorithm, which achieved a multiple-object tracking | 102 | DeepSORT tracking algorithm, which achieved a multiple-object tracking | ||
120 | accuracy (MOTA) ranging from 37% to 88% and multiple object tracking | 103 | accuracy (MOTA) ranging from 37% to 88% and multiple object tracking | ||
121 | precision (MOTP) between 63% and 86%. Results from this research | 104 | precision (MOTP) between 63% and 86%. Results from this research | ||
122 | indicate that deep learning technology can detect and track features | 105 | indicate that deep learning technology can detect and track features | ||
123 | more consistently than human annotators, allowing for larger datasets | 106 | more consistently than human annotators, allowing for larger datasets | ||
124 | to be processed within a fraction of the time while avoiding | 107 | to be processed within a fraction of the time while avoiding | ||
125 | discrepancies introduced by labeling fatigue or multiple human | 108 | discrepancies introduced by labeling fatigue or multiple human | ||
126 | annotators.", | 109 | annotators.", | ||
127 | "num_resources": 1, | 110 | "num_resources": 1, | ||
128 | "num_tags": 6, | 111 | "num_tags": 6, | ||
129 | "organization": { | 112 | "organization": { | ||
130 | "approval_status": "approved", | 113 | "approval_status": "approved", | ||
131 | "created": "2017-07-21T13:15:49.935872", | 114 | "created": "2017-07-21T13:15:49.935872", | ||
132 | "description": "The Centre for Earth Observation Science (CEOS) | 115 | "description": "The Centre for Earth Observation Science (CEOS) | ||
133 | was established in 1994 with a mandate to research, preserve and | 116 | was established in 1994 with a mandate to research, preserve and | ||
134 | communicate knowledge of Earth system processes using the technologies | 117 | communicate knowledge of Earth system processes using the technologies | ||
135 | of Earth Observation Science. Research is multidisciplinary and | 118 | of Earth Observation Science. Research is multidisciplinary and | ||
136 | collaborative seeking to understand the complex interrelationships | 119 | collaborative seeking to understand the complex interrelationships | ||
137 | between elements of Earth systems, and how these systems will likely | 120 | between elements of Earth systems, and how these systems will likely | ||
138 | respond to climate change. Although researchers have worked in many | 121 | respond to climate change. Although researchers have worked in many | ||
139 | regions, the Arctic marine system has always been a unifying focus of | 122 | regions, the Arctic marine system has always been a unifying focus of | ||
140 | activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate | 123 | activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate | ||
141 | Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre | 124 | Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre | ||
142 | (ARC, Aarhus, Denmark) established the Arctic Science Partnership, | 125 | (ARC, Aarhus, Denmark) established the Arctic Science Partnership, | ||
143 | thereby integrating academic and research initiatives.\r\n\r\nAreas of | 126 | thereby integrating academic and research initiatives.\r\n\r\nAreas of | ||
144 | existing research activity are divided among key themes:\r\n\r\nArctic | 127 | existing research activity are divided among key themes:\r\n\r\nArctic | ||
145 | Anthropology/Paleoclimatology: LiDAR scanning and digital site | 128 | Anthropology/Paleoclimatology: LiDAR scanning and digital site | ||
146 | preservation, archaeo-geophysics, permafrost degredation, lithic | 129 | preservation, archaeo-geophysics, permafrost degredation, lithic | ||
147 | morphometrics, zooarchaeology, proxy studies, paleodistribution of sea | 130 | morphometrics, zooarchaeology, proxy studies, paleodistribution of sea | ||
148 | ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, | 131 | ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, | ||
149 | ethnographic analogy, traditional knowledge, climate change and | 132 | ethnographic analogy, traditional knowledge, climate change and | ||
150 | northern heritage resource management.\r\n\r\nAtmospheric | 133 | northern heritage resource management.\r\n\r\nAtmospheric | ||
151 | Studies/Meteorology: Boundary layer, precipitation, clouds, storms and | 134 | Studies/Meteorology: Boundary layer, precipitation, clouds, storms and | ||
152 | extreme weather, circulation, eddy correlations, polar vortex, | 135 | extreme weather, circulation, eddy correlations, polar vortex, | ||
153 | climate, teleconnections, geophysical fluid dynamics, flux and energy | 136 | climate, teleconnections, geophysical fluid dynamics, flux and energy | ||
154 | budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice | 137 | budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice | ||
155 | albedo feedback, cloud radiative forcing, pCO2. | 138 | albedo feedback, cloud radiative forcing, pCO2. | ||
156 | \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, | 139 | \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, | ||
157 | Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace | 140 | Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace | ||
158 | metals, minerals, hydrocarbons, brine processes, otolith | 141 | metals, minerals, hydrocarbons, brine processes, otolith | ||
159 | microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, | 142 | microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, | ||
160 | trace metals, PAHs, source, transport, transformation, pathways, | 143 | trace metals, PAHs, source, transport, transformation, pathways, | ||
161 | bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth | 144 | bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth | ||
162 | Observation Science: Active and passive microwave, LiDAR, EM | 145 | Observation Science: Active and passive microwave, LiDAR, EM | ||
163 | induction, spatial-temporal analysis, forward and inverse scattering | 146 | induction, spatial-temporal analysis, forward and inverse scattering | ||
164 | models, complex permittivity, ocean colour, ocean surface roughness, | 147 | models, complex permittivity, ocean colour, ocean surface roughness, | ||
165 | NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: | 148 | NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: | ||
166 | Biophysical processes, primary production; ice algae, ice | 149 | Biophysical processes, primary production; ice algae, ice | ||
167 | microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland | 150 | microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland | ||
168 | Lakes and Waters: Hydrologic connectivity, watershed systems, sediment | 151 | Lakes and Waters: Hydrologic connectivity, watershed systems, sediment | ||
169 | transport, nutrient transport, contaminants, landscape processes, | 152 | transport, nutrient transport, contaminants, landscape processes, | ||
170 | remote sensing, freshwater-marine coupling. Marine Mammals: Seals, | 153 | remote sensing, freshwater-marine coupling. Marine Mammals: Seals, | ||
171 | whales, habitat, conservation, satellite telemetry, distribution, | 154 | whales, habitat, conservation, satellite telemetry, distribution, | ||
172 | population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: | 155 | population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: | ||
173 | Simulation of sea ice and oceanic regional processes, Nucleus for | 156 | Simulation of sea ice and oceanic regional processes, Nucleus for | ||
174 | European Modelling of the Ocean (NEMO), ice-ocean modelling and | 157 | European Modelling of the Ocean (NEMO), ice-ocean modelling and | ||
175 | interactions, hind cast simulations and projections for sea ice state | 158 | interactions, hind cast simulations and projections for sea ice state | ||
176 | and ocean variables based on CMIP5 scenarios and MIROC5 forcing, | 159 | and ocean variables based on CMIP5 scenarios and MIROC5 forcing, | ||
177 | validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and | 160 | validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and | ||
178 | out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy | 161 | out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy | ||
179 | correlations.\r\n\r\nSea Ice Geophysics:Thermodynamic and dynamic | 162 | correlations.\r\n\r\nSea Ice Geophysics:Thermodynamic and dynamic | ||
180 | processes, extreme ice features and hazards, snow, ridges, | 163 | processes, extreme ice features and hazards, snow, ridges, | ||
181 | polynyas.\r\n\r\nTraditional and Local Knowledge: Indigenous cultures, | 164 | polynyas.\r\n\r\nTraditional and Local Knowledge: Indigenous cultures, | ||
182 | Inuit, Inuvialuit, oral history, toponomy, mobility and settlement, | 165 | Inuit, Inuvialuit, oral history, toponomy, mobility and settlement, | ||
183 | hunting, food security, sea ice use, community-based research, | 166 | hunting, food security, sea ice use, community-based research, | ||
184 | community-based monitoring, two ways of knowing.", | 167 | community-based monitoring, two ways of knowing.", | ||
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188 | "name": "ceos2", | 171 | "name": "ceos2", | ||
189 | "state": "active", | 172 | "state": "active", | ||
190 | "title": "CEOS", | 173 | "title": "CEOS", | ||
191 | "type": "organization" | 174 | "type": "organization" | ||
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207 | "related_programs": "[]", | 190 | "related_programs": "[]", | ||
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217 | "description": "Churchill Beluga Boat Drone Imagery related | 200 | "description": "Churchill Beluga Boat Drone Imagery related | ||
218 | journal article published in Drone Systems and Applications.\r\nDOI: | 201 | journal article published in Drone Systems and Applications.\r\nDOI: | ||
219 | https://doi.org/10.1139/juvs-2021-0024", | 202 | https://doi.org/10.1139/juvs-2021-0024", | ||
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227 | "name": "Detection and tracking of belugas, kayaks and motorized | 210 | "name": "Detection and tracking of belugas, kayaks and motorized | ||
228 | boats in drone video using deep learning", | 211 | boats in drone video using deep learning", | ||
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248 | { | 231 | { | ||
n | n | 232 | "display_name": "Beluga", | ||
233 | "id": "a9f25a89-b0ef-4d4d-993d-73f28e0d702a", | ||||
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249 | "display_name": "Unmanned Aerial Vehicle", | 239 | "display_name": "Unmanned Aerial Vehicle", | ||
250 | "id": "a6dc9001-e6da-4a84-bfec-2941d3ebce78", | 240 | "id": "a6dc9001-e6da-4a84-bfec-2941d3ebce78", | ||
251 | "name": "Unmanned Aerial Vehicle", | 241 | "name": "Unmanned Aerial Vehicle", | ||
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253 | "vocabulary_id": null | ||||
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255 | { | ||||
256 | "display_name": "beluga", | ||||
257 | "id": "286b2f82-a071-41cc-bc3b-7ea986233649", | ||||
258 | "name": "beluga", | ||||
259 | "state": "active", | 242 | "state": "active", | ||
260 | "vocabulary_id": null | 243 | "vocabulary_id": null | ||
261 | }, | 244 | }, | ||
262 | { | 245 | { | ||
263 | "display_name": "computer vision", | 246 | "display_name": "computer vision", | ||
264 | "id": "d7270905-c420-4d19-aa9c-c6f818ab5b67", | 247 | "id": "d7270905-c420-4d19-aa9c-c6f818ab5b67", | ||
265 | "name": "computer vision", | 248 | "name": "computer vision", | ||
266 | "state": "active", | 249 | "state": "active", | ||
267 | "vocabulary_id": null | 250 | "vocabulary_id": null | ||
268 | }, | 251 | }, | ||
269 | { | 252 | { | ||
270 | "display_name": "deep learning", | 253 | "display_name": "deep learning", | ||
271 | "id": "87526358-2d8a-4c78-8375-38c132b53d5a", | 254 | "id": "87526358-2d8a-4c78-8375-38c132b53d5a", | ||
272 | "name": "deep learning", | 255 | "name": "deep learning", | ||
273 | "state": "active", | 256 | "state": "active", | ||
274 | "vocabulary_id": null | 257 | "vocabulary_id": null | ||
275 | }, | 258 | }, | ||
276 | { | 259 | { | ||
277 | "display_name": "object detection", | 260 | "display_name": "object detection", | ||
278 | "id": "a3d44586-cba5-4685-b07a-2d2f16578353", | 261 | "id": "a3d44586-cba5-4685-b07a-2d2f16578353", | ||
279 | "name": "object detection", | 262 | "name": "object detection", | ||
280 | "state": "active", | 263 | "state": "active", | ||
281 | "vocabulary_id": null | 264 | "vocabulary_id": null | ||
282 | }, | 265 | }, | ||
283 | { | 266 | { | ||
284 | "display_name": "object tracking", | 267 | "display_name": "object tracking", | ||
285 | "id": "28ce0864-2ed8-43d1-b80d-d79684cac63f", | 268 | "id": "28ce0864-2ed8-43d1-b80d-d79684cac63f", | ||
286 | "name": "object tracking", | 269 | "name": "object tracking", | ||
287 | "state": "active", | 270 | "state": "active", | ||
288 | "vocabulary_id": null | 271 | "vocabulary_id": null | ||
289 | } | 272 | } | ||
290 | ], | 273 | ], | ||
291 | "theme": [ | 274 | "theme": [ | ||
292 | "8f8cd877-b037-4b1a-b928-f86d9e093741", | 275 | "8f8cd877-b037-4b1a-b928-f86d9e093741", | ||
293 | "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", | 276 | "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", | ||
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295 | ], | 278 | ], | ||
296 | "title": "Detection and tracking of belugas, kayaks and motorized | 279 | "title": "Detection and tracking of belugas, kayaks and motorized | ||
297 | boats in drone video using deep learning", | 280 | boats in drone video using deep learning", | ||
298 | "type": "publication", | 281 | "type": "publication", | ||
299 | "url": null, | 282 | "url": null, | ||
300 | "version": null | 283 | "version": null | ||
301 | } | 284 | } |