IP Library Granted Patent US 12,192,634
Granted Patent B2
US 12,192,634 · App. 18/206,542 · Granted Jan 7, 2025

Automated camera positioning for feeding behavior monitoring

Inventors: Barnaby John James (Los Gatos, CA); Grace Taixi Brentano (Redwood City, CA); Laura Valentine Chrobak (Menlo Park, CA); Zhaoying Yao (Palo Alto, CA)
Assignee: TidaIX AI Inc.
H04N23/695A01K61/80G06T7/80H04N7/18G06T2207/30232G06T2207/30244
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Quick Facts
Patent No.
US 12,192,634
App. No.
18/206,542
Granted
Jan 7, 2025
Kind
B2
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for automated camera positioning for feeding behavior monitoring. In some implementations, a system obtains an image of a scene, a spatial model that corresponds to a subfeeder, and calibration parameters of a camera, the system determines a size of the subfeeder in the image of the scene, the system selects an updated position of the camera relative to the subfeeder, the system provides the updated position of the camera relative to the subfeeder to a winch controller, and the system moves the camera to the updated position.

Claims (62)

1. A method comprising:

obtaining, at one or more processing devices, an image of a scene that includes an aquaculture subfeeder configured to deliver a feed underwater;

obtaining, by the one or more processing devices, a spatial model that corresponds to the subfeeder;

obtaining calibration parameters of a camera that captured the image of the scene;

determining, by the one or more processing devices, and based on the spatial model, the image of the scene, and the calibration parameters of the camera, a size of the subfeeder in the image of the scene;

selecting, by the one or more processing devices, and based on the size of the subfeeder in the image of the scene, an updated position of the camera relative to the subfeeder;

generating, by the one or more processing devices, a signal configured to cause a winch controller to move the camera to the updated position; and

providing the signal to the winch controller.

2. The method of claim 1 , wherein selecting, by the one or more processing devices, and based on the size of the subfeeder in the image of the scene, the updated position of the camera comprises:

dynamically selecting the updated position of the camera relative to the subfeeder.

3. The method of claim 1 , wherein the updated position comprises a preferred position of the camera relative to the subfeeder associated with the spatial model of the subfeeder.

4. The method of claim 1 , wherein selecting, by the one or more processing devices, and based on the size of the subfeeder in the image of the scene, the updated position of the camera relative to the subfeeder, comprises:

determining a depth of the subfeeder that characterizes a distance of the camera that obtained the image of the scene from the subfeeder;

determining, based on the depth of the subfeeder, a current position of the camera relative to the subfeeder; and

selecting, based on the current position of the camera relative to the subfeeder, the updated position of the camera relative to the subfeeder.

5. The method of claim 4 , wherein determining the depth of the subfeeder that characterizes the distance of the camera that obtained the image of the scene from the subfeeder comprises:

determining a number of pixels in the image of the scene that depicts the subfeeder.

6. The method of claim 4 , wherein determining the depth of the subfeeder that characterizes the distance of the camera that obtained the image of the scene from the subfeeder comprises:

correlating the spatial model of the subfeeder within the image of the scene with the calibration parameters of the camera.

7. The method of claim 1 , wherein, in the image of the scene, the subfeeder is at least partially obscured by another object.

8. The method of claim 1 , further comprising:

determining, by the one or more processing devices, and based on the spatial model, the image of the scene, and the calibration parameters of the camera, a current position of the camera relative to the subfeeder, and

wherein selecting, by the one or more processing devices, and based on the size of the subfeeder in the image of the scene, the updated position of the camera relative to the subfeeder comprises:

selecting the updated position of the camera relative to the subfeeder such that a feed is visible within the image of the scene.

9. The method of claim 8 , wherein determining, by the one or more processing devices, and based on the spatial model, the image of the scene, and the calibration parameters of the camera, the current position of the camera relative to the subfeeder comprises:

determining that at the current position of the camera relative to the subfeeder a feed is not visible within the image of the scene.

10. A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

obtaining an image of a scene that includes an aquaculture subfeeder configured to deliver a feed underwater;

obtaining a spatial model that corresponds to the subfeeder;

obtaining calibration parameters of a camera that captured the image of the scene;

determining, based on the spatial model, the image of the scene, and the calibration parameters of the camera, a size of the subfeeder in the image of the scene;

selecting, based on the size of the subfeeder in the image of the scene, an updated position of the camera relative to the subfeeder;

generating a signal configured to cause a winch controller to move the camera to the updated position; and

providing the signal to the winch controller.

11. The system of claim 10 , wherein selecting, based on the size of the subfeeder in the image of the scene, the updated position of the camera comprises:

dynamically selecting the updated position of the camera relative to the subfeeder.

12. The system of claim 10 , wherein the updated position comprises a preferred position of the camera relative to the subfeeder associated with the spatial model of the subfeeder.

13. The system of claim 10 , wherein selecting, based on the size of the subfeeder in the image of the scene, the updated position of the camera relative to the subfeeder, comprises:

determining a depth of the subfeeder that characterizes a distance of the camera that obtained the image of the scene from the subfeeder;

determining, based on the depth of the subfeeder, a current position of the camera relative to the subfeeder; and

selecting, based on the current position of the camera relative to the subfeeder, the updated position of the camera relative to the subfeeder.

14. The system of claim 13 , wherein determining the depth of the subfeeder that characterizes the distance of the camera that obtained the image of the scene from the subfeeder comprises:

determining a number of pixels in the image of the scene that depict the subfeeder.

15. The system of claim 13 , wherein determining the depth of the subfeeder that characterizes the distance of the camera that obtained the image of the scene from the subfeeder comprises:

correlating the spatial model of the subfeeder within the image of the scene with the calibration parameters of the camera.

16. One or more non-transitory computer-readable storage medium coupled to one or more processors that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

obtaining an image of a scene that includes an aquaculture subfeeder configured to deliver a feed underwater;

obtaining a spatial model that corresponds to the subfeeder;

obtaining calibration parameters of a camera that captured the image of the scene;

determining, based on the spatial model, the image of the scene, and the calibration parameters of the camera, a size of the subfeeder in the image of the scene;

selecting, based on the size of the subfeeder in the image of the scene, an updated position of the camera relative to the subfeeder;

generating a signal configured to cause a winch controller to move the camera to the updated position; and

providing the signal to the winch controller.

17. The one or more non-transitory computer-readable storage medium of claim 16 , wherein selecting, based on the size of the subfeeder in the image of the scene, the updated position of the camera comprises:

dynamically selecting the updated position of the camera relative to the subfeeder.

18. The one or more non-transitory computer-readable storage medium of claim 16 , wherein the updated position comprises a preferred position of the camera relative to the subfeeder associated with the spatial model of the subfeeder.

19. The one or more non-transitory computer-readable storage medium of claim 16 , wherein selecting, based on the size of the subfeeder in the image of the scene, the updated position of the camera relative to the subfeeder, comprises:

determining a depth of the subfeeder that characterizes a distance of the camera that obtained the image of the scene from the subfeeder;

determining, based on the depth of the subfeeder, a current position of the camera relative to the subfeeder; and

selecting, based on the current position of the camera relative to the subfeeder, the updated position of the camera relative to the subfeeder.

20. The one or more non-transitory computer-readable storage medium of claim 19 , wherein determining the depth of the subfeeder that characterizes the distance of the camera that obtained the image of the scene from the subfeeder comprises:

determining a number of pixels in the image of the scene that depict the subfeeder.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 22, 2024
From: X DEVELOPMENT LLC
To: TIDALX AI INC.
Reel/Frame 068477/0306 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 14, 2023
From: JAMES, BARNABY JOHN; BRENTANO, GRACE TAIXI; CHROBAK, LAURA VALENTINE; YAO, ZHAOYING
To: X DEVELOPMENT LLC
Reel/Frame 063947/0333 →
Continuity (3)
Continuation 17691902 · Mar 10, 2022
Continuation 17246911 · May 3, 2021
Related Publication 20230388639A1 · Nov 30, 2023
References Cited (106)
US 6000362A · Blyth et al. · 1999 [cited by applicant]
US 9650155B2 · Wang et al. · 2017 [cited by applicant]
US 10856520B1 · Kozachenok et al. · 2020 [cited by applicant]
US 11148294B2 · Hayash · 2021 [cited by applicant]
US 20070003162A1 · Miyoshi et al. · 2007 [cited by applicant]
US 20090296989A1 · Ramesh et al. · 2009 [cited by applicant]
US 20100198023A1 · Yanai et al. · 2010 [cited by applicant]
US 20120006277A1 · Troy et al. · 2012 [cited by applicant]
US 20120055412A1 · Chen et al. · 2012 [cited by applicant]
US 20120314030A1 · Datta et al. · 2012 [cited by applicant]
US 20130206078A1 · Melberg et al. · 2013 [cited by applicant]
US 20130284105A1 · Han et al. · 2013 [cited by applicant]
US 20150302241A1 · Eineren et al. · 2015 [cited by applicant]
US 20170013810A1 · Grabell · 2017 [cited by examiner]
US 20170105388A1 · Pfeiff · 2017 [cited by applicant]
US 20170150701A1 · Gilmore et al. · 2017 [cited by applicant]
US 20170169604A1 · Van Der Zwan et al. · 2017 [cited by applicant]
US 20180107210A1 · Harnett · 2018 [cited by applicant]
US 20180132459A1 · Baba · 2018 [cited by applicant]
US 20190021292A1 · Hayes · 2019 [cited by applicant]
US 20190025858A1 · Bar-Nahum et al. · 2019 [cited by applicant]
US 20190096069A1 · Qian et al. · 2019 [cited by applicant]
US 20190228218A1 · Barnaby et al. · 2019 [cited by applicant]
US 20190340440A1 · Atwater et al. · 2019 [cited by applicant]
US 20190349533A1 · Guo et al. · 2019 [cited by applicant]
US 20200107524A1 · Messana et al. · 2020 [cited by applicant]
US 20200113158A1 · Rishi · 2020 [cited by applicant]
US 20200155882A1 · Tohidi et al. · 2020 [cited by applicant]
US 20200288678A1 · Howe et al. · 2020 [cited by applicant]
US 20200323183A1 · Tvedt · 2020 [cited by examiner]
US 20210215139A1 · Roodenburg et al. · 2021 [cited by applicant]
US 20210279860A1 · Ye et al. · 2021 [cited by applicant]
US 20220272255A1 · Xiong et al. · 2022 [cited by applicant]
US 20230284600A1 · Nguyen · 2023 [cited by examiner]
CN 103883138 · 2014 [cited by applicant]
CN 106454268 · 2017 [cited by applicant]
CN 107593559 · 2018 [cited by applicant]
CN 107996528 · 2018 [cited by applicant]
CN 108040948 · 2018 [cited by applicant]
CN 108338110 · 2018 [cited by applicant]
CN 207940232 · 2018 [cited by applicant]
CN 109757415 · 2019 [cited by applicant]
CN 110476870 · 2019 [cited by applicant]
CN 209768606 · 2019 [cited by applicant]
CN 209803122 · 2019 [cited by applicant]
CN 110870472 · 2020 [cited by applicant]
CN 110881434 · 2020 [cited by applicant]
CN 106665447 · 2020 [cited by applicant]
CN 111789063 · 2020 [cited by applicant]
CN 111838044 · 2020 [cited by applicant]
CN 212060219 · 2020 [cited by applicant]
EP 2244934 · 2010 [cited by applicant]
EP 3484283 · 2019 [cited by applicant]
JP 2002171853 · 2002 [cited by applicant]
KR 200392371 · 2005 [cited by applicant]
KR 100946942 · 2010 [cited by applicant]
KR 20200089195 · 2020 [cited by applicant]
NO 300401 · 1997 [cited by applicant]
NO 345829 · 2019 [cited by applicant]
TW 1721851 · 1991 [cited by applicant]
TW M603271 · 2020 [cited by applicant]
WO WO1990007874 · 1990 [cited by applicant]
WO WO1997019587 · 1997 [cited by applicant]
WO WO2009008733 · 2009 [cited by applicant]
WO WO2009097057 · 2009 [cited by applicant]
WO WO2014179482 · 2014 [cited by applicant]
WO WO2016023071 · 2016 [cited by applicant]
WO WO2018011744 · 2018 [cited by applicant]
WO WO2019002881 · 2019 [cited by applicant]
WO WO2019121851 · 2019 [cited by applicant]
WO WO2019188506 · 2019 [cited by applicant]
WO WO2019212807 · 2019 [cited by applicant]
WO WO2019232247 · 2019 [cited by applicant]
WO WO2020046524 · 2020 [cited by applicant]
WO WO2020072438 · 2020 [cited by applicant]
WO WO2020132031 · 2020 [cited by applicant]
WO WO2021006744 · 2021 [cited by applicant]
WO WO2021030237 · 2021 [cited by applicant]
WO WO2022010815 · 2022 [cited by applicant]
International Preliminary Report on Patentability in International Appln No. PCT/US2022/022250, dated Nov. 16, 2023, 10 pages. [cited by applicant]
[No Author Listed] “Placentia Bay Atlantic Salmon Aquaculture Project Environmental Effects Monitoring Plan: Benthic Habitat Health,” LGL Limited, LGL Rep. FA0159B, Jun. 2019, 61 pages. [cited by applicant]
Extended Search Report in European Appln. No. 22151132.2, dated May 2, 2022, 10 pages. [cited by applicant]
Fore et al. Precision fish farming: A new framework to improve production in aquaculture, Biosystems Engineering vol. 173, pp. 176-193 (Year: 2018). [cited by applicant]
https://akvapartner.no [online], “Newly Patented Brattland Subfeeder,” Sep. 2020, retrieved on Apr. 26, 2021, retrieved from URL<http://https://akvapartner.no/en/b/subfeeder>, 6 pages. [cited by applicant]
International Preliminary Report on Patentability in International AppIn No. PCT/US2020/059829, dated May 27, 2022, 11 pages. [cited by applicant]
International Search Report and Written Opinion in International Appln No. PCT/US2022/022837, dated Aug. 2, 2022, 14 pages. [cited by applicant]
International Search Report and Written Opinion International Appln No. PCT/US2020/059829, dated Feb. 25, 2021, 18 pages. [cited by applicant]
International Search Report and Written Opinion International Appln No. PCT/US2022/018651, dated Jun. 22, 2022, 14 pages. [cited by applicant]
International Search Report and Written Opinion International AppIn No. PCT/US2022/021683, dated Jun. 27, 2022, 14 pages. [cited by applicant]
International Search Report and Written Opinion International Appln No. PCT/US2022/022250, dated Jul. 6, 2022, 15 pages. [cited by applicant]
International Search Report and Written Opinion International Appln No. PCT/US2022/022492, dated Jun. 28, 2022, 13 pages. [cited by applicant]
International Search Report and Written Opinion International AppIn No. PCT/US2022/022589, dated Jul. 7, 2022, 12 pages. [cited by applicant]
International Search Report and Written Opinion International Appln No. PCT/US2022/023831, dated Jul. 8, 2022, 13 pages. [cited by applicant]
Li et al., “Intelligent aquaculture,” JWAS, Aug. 2020, 51(4):808-814. [cited by applicant]
Maloy et al., “A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture,” Computers and Electronics in Agriculture, Nov. 12, 2019, 9 pages. [cited by applicant]
Meidell et al., “FishNet: A Unified Embedding for Salmon Recognition,” Thesis for Master's degree in Artificial Intelligence, Norwegian University of Science and Technology, Jun. 2019, 86 pages. [cited by applicant]
Moskvyak et al., “Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings,” CoRR, Feb. 2019, arXiv:1902.10847v1, 12 pages. [cited by applicant]
Odey, “AquaMesh—Design and Implementation of Smart Wireless Mesh Sensor Networks for Aquaculture,” American Journal of Networks and Communications, Jul. 2013, 8 pages. [cited by applicant]
Office Action in Canadian Appln. No. 3,087,370, dated Aug. 4, 2021, 3 pages. [cited by applicant]
Petrov et al., “Overview of the application of computer vision technology in fish farming,” E3S Web of Conferences, 2020, 175:02015. [cited by applicant]
Qiu et al., “Improving Transfer Learning and Squeeze-and-Excitation Networks for Small-Scale Fine-Grained Fish Image Classification,” IEEE Access, Dec. 2018, 6(31):78503-78512. [cited by applicant]
Saberloon et al., “Application of Machine Vision Systems in Aquaculture with Emphasis on Fish: State-of-the-Art and Key Issues,” Reviews in Aquaculture, Dec. 2017, 9:369-387. [cited by applicant]
Stein et al., “Consistent melanophore spot patterns allow long-term individual recognition of Atlantic salmon Salmo Salar,” Journal of Fish Biology, Nov. 2017, 91(6):1699-1712. [cited by applicant]
towardsdatascience.com [online], “Analyzing Applications of Deep Learning in Aquaculture,” Jan. 2021, retrieved on Aug. 11, 2021, retrieved from URL<https://towardsdatascience.com/analyzing-applications-of-deep-learning… [cited by applicant]
Wang, “Robust tracking of fish schools using CNN for head identification,” Multimedia Tools and Applications, Nov. 2017, 20 pages. [cited by applicant]
www.akvagroup.com [online], “Subsea Feeding,” Feb. 2019, retrieved on Apr. 26, 2021, retrieved from URL<https://www.akvagroup.com/pen-based-aquaculture/feed-systems/subsea-feeding>, 5 pages. [cited by applicant]
Cited By (1)
US 12,656,744