Changes
On April 7, 2022 at 2:49:14 PM CDT, Casey Clair:
-
Added resource Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning to 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", | ||
9 | "nameIdentifierScheme": "ORCID", | 9 | "nameIdentifierScheme": "ORCID", | ||
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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": "", | ||
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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", | ||
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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", | ||
55 | "creator_user_id": "cde7b848-a882-4fc7-97c9-670417bd6b43", | 55 | "creator_user_id": "cde7b848-a882-4fc7-97c9-670417bd6b43", | ||
56 | "descriptionType": "Abstract", | 56 | "descriptionType": "Abstract", | ||
57 | "funderIdentifier": "", | 57 | "funderIdentifier": "", | ||
58 | "funderIdentifierType": "", | 58 | "funderIdentifierType": "", | ||
59 | "funderName": "", | 59 | "funderName": "", | ||
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61 | "grantNumber": "", | 61 | "grantNumber": "", | ||
62 | "groups": [ | 62 | "groups": [ | ||
63 | { | 63 | { | ||
64 | "description": "Inland water features, drainage systems and | 64 | "description": "Inland water features, drainage systems and | ||
65 | their characteristics. Examples of data you can find here include | 65 | their characteristics. Examples of data you can find here include | ||
66 | river and lake data, water quality data. \r\n\r\nIn CEOS, related | 66 | river and lake data, water quality data. \r\n\r\nIn CEOS, related | ||
67 | research themes include biogeochemistry, Inland lakes and waters, | 67 | research themes include biogeochemistry, Inland lakes and waters, | ||
68 | modelling, remote sensing and technology, trace metals and | 68 | modelling, remote sensing and technology, trace metals and | ||
69 | contaminants.", | 69 | contaminants.", | ||
70 | "display_name": "Freshwater", | 70 | "display_name": "Freshwater", | ||
71 | "id": "8f8cd877-b037-4b1a-b928-f86d9e093741", | 71 | "id": "8f8cd877-b037-4b1a-b928-f86d9e093741", | ||
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74 | "name": "freshwater", | 74 | "name": "freshwater", | ||
75 | "title": "Freshwater" | 75 | "title": "Freshwater" | ||
76 | }, | 76 | }, | ||
77 | { | 77 | { | ||
78 | "description": "Features and characteristics of salt water | 78 | "description": "Features and characteristics of salt water | ||
79 | bodies.\r\n\r\nIn CEOS, related research themes include | 79 | bodies.\r\n\r\nIn CEOS, related research themes include | ||
80 | biogeochemistry, modelling, marine mammals, oil spill response, | 80 | biogeochemistry, modelling, marine mammals, oil spill response, | ||
81 | physical oceanography, remote sensing and technology and trace metals | 81 | physical oceanography, remote sensing and technology and trace metals | ||
82 | and contaminants", | 82 | and contaminants", | ||
83 | "display_name": "Marine", | 83 | "display_name": "Marine", | ||
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87 | "name": "marine", | 87 | "name": "marine", | ||
88 | "title": "Marine" | 88 | "title": "Marine" | ||
89 | } | 89 | } | ||
90 | ], | 90 | ], | ||
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92 | "isopen": false, | 92 | "isopen": false, | ||
93 | "keywords": "computer vision,deep learning,Unmanned Aerial | 93 | "keywords": "computer vision,deep learning,Unmanned Aerial | ||
94 | Vehicle,beluga,object detection,object tracking", | 94 | Vehicle,beluga,object detection,object tracking", | ||
95 | "language": "English", | 95 | "language": "English", | ||
96 | "licenceType": "Open", | 96 | "licenceType": "Open", | ||
97 | "license_id": null, | 97 | "license_id": null, | ||
98 | "license_title": null, | 98 | "license_title": null, | ||
99 | "maintainer": null, | 99 | "maintainer": null, | ||
100 | "maintainer_email": null, | 100 | "maintainer_email": null, | ||
101 | "metadata_created": "2022-04-07T19:45:13.021227", | 101 | "metadata_created": "2022-04-07T19:45:13.021227", | ||
n | 102 | "metadata_modified": "2022-04-07T19:45:13.021234", | n | 102 | "metadata_modified": "2022-04-07T19:49:13.958157", |
103 | "name": | 103 | "name": | ||
104 | elugas-kayaks-and-motorized-boats-in-drone-video-using-deep-learning", | 104 | elugas-kayaks-and-motorized-boats-in-drone-video-using-deep-learning", | ||
105 | "notes": "\"Aerial imagery surveys are commonly used in marine | 105 | "notes": "\"Aerial imagery surveys are commonly used in marine | ||
106 | mammal research to determine population size, distribution and habitat | 106 | mammal research to determine population size, distribution and habitat | ||
107 | use. Analysis of aerial photos involves hours of manually identifying | 107 | use. Analysis of aerial photos involves hours of manually identifying | ||
108 | individuals present in each image and converting raw counts into | 108 | individuals present in each image and converting raw counts into | ||
109 | useable biological statistics. Our research proposes the use of deep | 109 | useable biological statistics. Our research proposes the use of deep | ||
110 | learning algorithms to increase the efficiency of the marine mammal | 110 | learning algorithms to increase the efficiency of the marine mammal | ||
111 | research workflow. To test the feasibility of this proposal, the | 111 | research workflow. To test the feasibility of this proposal, the | ||
112 | existing YOLOv4 convolutional neural network model was trained to | 112 | existing YOLOv4 convolutional neural network model was trained to | ||
113 | detect belugas, kayaks and motorized boats in oblique drone imagery, | 113 | detect belugas, kayaks and motorized boats in oblique drone imagery, | ||
114 | collected from a stationary tethered system. Automated computer-based | 114 | collected from a stationary tethered system. Automated computer-based | ||
115 | object detection achieved the following precision and recall, | 115 | object detection achieved the following precision and recall, | ||
116 | respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and | 116 | respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and | ||
117 | kayak = 96%/96%. We then tested the performance of computer vision | 117 | kayak = 96%/96%. We then tested the performance of computer vision | ||
118 | tracking of belugas and occupied watercraft in drone videos using the | 118 | tracking of belugas and occupied watercraft in drone videos using the | ||
119 | DeepSORT tracking algorithm, which achieved a multiple-object tracking | 119 | DeepSORT tracking algorithm, which achieved a multiple-object tracking | ||
120 | accuracy (MOTA) ranging from 37% to 88% and multiple object tracking | 120 | accuracy (MOTA) ranging from 37% to 88% and multiple object tracking | ||
121 | precision (MOTP) between 63% and 86%. Results from this research | 121 | precision (MOTP) between 63% and 86%. Results from this research | ||
122 | indicate that deep learning technology can detect and track features | 122 | indicate that deep learning technology can detect and track features | ||
123 | more consistently than human annotators, allowing for larger datasets | 123 | more consistently than human annotators, allowing for larger datasets | ||
124 | to be processed within a fraction of the time while avoiding | 124 | to be processed within a fraction of the time while avoiding | ||
125 | discrepancies introduced by labeling fatigue or multiple human | 125 | discrepancies introduced by labeling fatigue or multiple human | ||
126 | annotators.\"", | 126 | annotators.\"", | ||
n | 127 | "num_resources": 0, | n | 127 | "num_resources": 1, |
128 | "num_tags": 6, | 128 | "num_tags": 6, | ||
129 | "organization": { | 129 | "organization": { | ||
130 | "approval_status": "approved", | 130 | "approval_status": "approved", | ||
131 | "created": "2017-07-21T13:15:49.935872", | 131 | "created": "2017-07-21T13:15:49.935872", | ||
132 | "description": "The Centre for Earth Observation Science (CEOS) | 132 | "description": "The Centre for Earth Observation Science (CEOS) | ||
133 | was established in 1994 with a mandate to research, preserve and | 133 | was established in 1994 with a mandate to research, preserve and | ||
134 | communicate knowledge of Earth system processes using the technologies | 134 | communicate knowledge of Earth system processes using the technologies | ||
135 | of Earth Observation Science. Research is multidisciplinary and | 135 | of Earth Observation Science. Research is multidisciplinary and | ||
136 | collaborative seeking to understand the complex interrelationships | 136 | collaborative seeking to understand the complex interrelationships | ||
137 | between elements of Earth systems, and how these systems will likely | 137 | between elements of Earth systems, and how these systems will likely | ||
138 | respond to climate change. Although researchers have worked in many | 138 | respond to climate change. Although researchers have worked in many | ||
139 | regions, the Arctic marine system has always been a unifying focus of | 139 | 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 | 140 | activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate | ||
141 | Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre | 141 | Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre | ||
142 | (ARC, Aarhus, Denmark) established the Arctic Science Partnership, | 142 | (ARC, Aarhus, Denmark) established the Arctic Science Partnership, | ||
143 | thereby integrating academic and research initiatives.\r\n\r\nAreas of | 143 | thereby integrating academic and research initiatives.\r\n\r\nAreas of | ||
144 | existing research activity are divided among key themes:\r\n\r\nArctic | 144 | existing research activity are divided among key themes:\r\n\r\nArctic | ||
145 | Anthropology/Paleoclimatology: LiDAR scanning and digital site | 145 | Anthropology/Paleoclimatology: LiDAR scanning and digital site | ||
146 | preservation, archaeo-geophysics, permafrost degredation, lithic | 146 | preservation, archaeo-geophysics, permafrost degredation, lithic | ||
147 | morphometrics, zooarchaeology, proxy studies, paleodistribution of sea | 147 | morphometrics, zooarchaeology, proxy studies, paleodistribution of sea | ||
148 | ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, | 148 | ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, | ||
149 | ethnographic analogy, traditional knowledge, climate change and | 149 | ethnographic analogy, traditional knowledge, climate change and | ||
150 | northern heritage resource management.\r\n\r\nAtmospheric | 150 | northern heritage resource management.\r\n\r\nAtmospheric | ||
151 | Studies/Meteorology: Boundary layer, precipitation, clouds, storms and | 151 | Studies/Meteorology: Boundary layer, precipitation, clouds, storms and | ||
152 | extreme weather, circulation, eddy correlations, polar vortex, | 152 | extreme weather, circulation, eddy correlations, polar vortex, | ||
153 | climate, teleconnections, geophysical fluid dynamics, flux and energy | 153 | climate, teleconnections, geophysical fluid dynamics, flux and energy | ||
154 | budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice | 154 | budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice | ||
155 | albedo feedback, cloud radiative forcing, pCO2. | 155 | albedo feedback, cloud radiative forcing, pCO2. | ||
156 | \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, | 156 | \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, | ||
157 | Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace | 157 | Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace | ||
158 | metals, minerals, hydrocarbons, brine processes, otolith | 158 | metals, minerals, hydrocarbons, brine processes, otolith | ||
159 | microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, | 159 | microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, | ||
160 | trace metals, PAHs, source, transport, transformation, pathways, | 160 | trace metals, PAHs, source, transport, transformation, pathways, | ||
161 | bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth | 161 | bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth | ||
162 | Observation Science: Active and passive microwave, LiDAR, EM | 162 | Observation Science: Active and passive microwave, LiDAR, EM | ||
163 | induction, spatial-temporal analysis, forward and inverse scattering | 163 | induction, spatial-temporal analysis, forward and inverse scattering | ||
164 | models, complex permittivity, ocean colour, ocean surface roughness, | 164 | models, complex permittivity, ocean colour, ocean surface roughness, | ||
165 | NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: | 165 | NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: | ||
166 | Biophysical processes, primary production; ice algae, ice | 166 | Biophysical processes, primary production; ice algae, ice | ||
167 | microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland | 167 | microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland | ||
168 | Lakes and Waters: Hydrologic connectivity, watershed systems, sediment | 168 | Lakes and Waters: Hydrologic connectivity, watershed systems, sediment | ||
169 | transport, nutrient transport, contaminants, landscape processes, | 169 | transport, nutrient transport, contaminants, landscape processes, | ||
170 | remote sensing, freshwater-marine coupling. Marine Mammals: Seals, | 170 | remote sensing, freshwater-marine coupling. Marine Mammals: Seals, | ||
171 | whales, habitat, conservation, satellite telemetry, distribution, | 171 | whales, habitat, conservation, satellite telemetry, distribution, | ||
172 | population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: | 172 | population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: | ||
173 | Simulation of sea ice and oceanic regional processes, Nucleus for | 173 | Simulation of sea ice and oceanic regional processes, Nucleus for | ||
174 | European Modelling of the Ocean (NEMO), ice-ocean modelling and | 174 | European Modelling of the Ocean (NEMO), ice-ocean modelling and | ||
175 | interactions, hind cast simulations and projections for sea ice state | 175 | interactions, hind cast simulations and projections for sea ice state | ||
176 | and ocean variables based on CMIP5 scenarios and MIROC5 forcing, | 176 | and ocean variables based on CMIP5 scenarios and MIROC5 forcing, | ||
177 | validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and | 177 | validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and | ||
178 | out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy | 178 | out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy | ||
179 | correlations.\r\n\r\nSea Ice Geophysics:Thermodynamic and dynamic | 179 | correlations.\r\n\r\nSea Ice Geophysics:Thermodynamic and dynamic | ||
180 | processes, extreme ice features and hazards, snow, ridges, | 180 | processes, extreme ice features and hazards, snow, ridges, | ||
181 | polynyas.\r\n\r\nTraditional and Local Knowledge: Indigenous cultures, | 181 | polynyas.\r\n\r\nTraditional and Local Knowledge: Indigenous cultures, | ||
182 | Inuit, Inuvialuit, oral history, toponomy, mobility and settlement, | 182 | Inuit, Inuvialuit, oral history, toponomy, mobility and settlement, | ||
183 | hunting, food security, sea ice use, community-based research, | 183 | hunting, food security, sea ice use, community-based research, | ||
184 | community-based monitoring, two ways of knowing.", | 184 | community-based monitoring, two ways of knowing.", | ||
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239 | }, | 266 | }, | ||
240 | { | 267 | { | ||
241 | "display_name": "deep learning", | 268 | "display_name": "deep learning", | ||
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243 | "name": "deep learning", | 270 | "name": "deep learning", | ||
244 | "state": "active", | 271 | "state": "active", | ||
245 | "vocabulary_id": null | 272 | "vocabulary_id": null | ||
246 | }, | 273 | }, | ||
247 | { | 274 | { | ||
248 | "display_name": "object detection", | 275 | "display_name": "object detection", | ||
249 | "id": "a3d44586-cba5-4685-b07a-2d2f16578353", | 276 | "id": "a3d44586-cba5-4685-b07a-2d2f16578353", | ||
250 | "name": "object detection", | 277 | "name": "object detection", | ||
251 | "state": "active", | 278 | "state": "active", | ||
252 | "vocabulary_id": null | 279 | "vocabulary_id": null | ||
253 | }, | 280 | }, | ||
254 | { | 281 | { | ||
255 | "display_name": "object tracking", | 282 | "display_name": "object tracking", | ||
256 | "id": "28ce0864-2ed8-43d1-b80d-d79684cac63f", | 283 | "id": "28ce0864-2ed8-43d1-b80d-d79684cac63f", | ||
257 | "name": "object tracking", | 284 | "name": "object tracking", | ||
258 | "state": "active", | 285 | "state": "active", | ||
259 | "vocabulary_id": null | 286 | "vocabulary_id": null | ||
260 | } | 287 | } | ||
261 | ], | 288 | ], | ||
262 | "theme": [ | 289 | "theme": [ | ||
263 | "8f8cd877-b037-4b1a-b928-f86d9e093741", | 290 | "8f8cd877-b037-4b1a-b928-f86d9e093741", | ||
264 | "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", | 291 | "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", | ||
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266 | ], | 293 | ], | ||
267 | "title": "Detection and tracking of belugas, kayaks and motorized | 294 | "title": "Detection and tracking of belugas, kayaks and motorized | ||
268 | boats in drone video using deep learning", | 295 | boats in drone video using deep learning", | ||
269 | "type": "publication", | 296 | "type": "publication", | ||
270 | "url": null, | 297 | "url": null, | ||
271 | "version": null | 298 | "version": null | ||
272 | } | 299 | } |