WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion

dc.contributor.advisorPatel, Amir
dc.contributor.authorJoska, Daniel
dc.date.accessioned2024-05-02T09:15:09Z
dc.date.available2024-05-02T09:15:09Z
dc.date.issued2023
dc.date.updated2024-04-30T13:09:40Z
dc.description.abstractIn many fields of research, it is often desirable to estimate the 3D pose of a subject - human, animal, or otherwise. Methods for obtaining accurate 3D pose data of a subject are broad in their applications; they inform the design of bio-mimetic robots, they aid greatly in bio-mechanical research, and they are used commonly in the study of animal neuroscience. Currently, robust methods for long-range tracking of subjects in the wild are few and far between, given the rarity of specific training data as well as the generally challenging nature of the associated computer vision problems. This thesis describes the design, implementation, and testing of both a hardware and software component to a method for the 3D motion capture of cheetahs in the wild, dubbed WildPose. The method makes use of multi-sensor fusion including lidar, RGB and IMU sensors to compensate for situations where pure vision-based techniques perform inadequately. To increase robustness, the software design makes use of previously successful trajectory optimisation techniques to yield accurate pose data in adverse conditions that would otherwise be extremely difficult to obtain. The method is extendable to other species with minimal variations. We demonstrate the method's efficacy through experimental validation on challenging cheetah locomotion datasets collected in the wild, presenting both qualitative and quantitative analyses for varied movements, environments, and lighting conditions. Through the shown effectiveness of these techniques in our specific use case, we aim to prove that the methods used perform admirably even under the trickiest of reconstruction problems. Thereby, we present our findings on cheetahs as a promising blueprint for the 3D motion capture of other species.
dc.identifier.apacitationJoska, D. (2023). <i>WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/39568en_ZA
dc.identifier.chicagocitationJoska, Daniel. <i>"WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2023. http://hdl.handle.net/11427/39568en_ZA
dc.identifier.citationJoska, D. 2023. WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion. . ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/39568en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Joska, Daniel AB - In many fields of research, it is often desirable to estimate the 3D pose of a subject - human, animal, or otherwise. Methods for obtaining accurate 3D pose data of a subject are broad in their applications; they inform the design of bio-mimetic robots, they aid greatly in bio-mechanical research, and they are used commonly in the study of animal neuroscience. Currently, robust methods for long-range tracking of subjects in the wild are few and far between, given the rarity of specific training data as well as the generally challenging nature of the associated computer vision problems. This thesis describes the design, implementation, and testing of both a hardware and software component to a method for the 3D motion capture of cheetahs in the wild, dubbed WildPose. The method makes use of multi-sensor fusion including lidar, RGB and IMU sensors to compensate for situations where pure vision-based techniques perform inadequately. To increase robustness, the software design makes use of previously successful trajectory optimisation techniques to yield accurate pose data in adverse conditions that would otherwise be extremely difficult to obtain. The method is extendable to other species with minimal variations. We demonstrate the method's efficacy through experimental validation on challenging cheetah locomotion datasets collected in the wild, presenting both qualitative and quantitative analyses for varied movements, environments, and lighting conditions. Through the shown effectiveness of these techniques in our specific use case, we aim to prove that the methods used perform admirably even under the trickiest of reconstruction problems. Thereby, we present our findings on cheetahs as a promising blueprint for the 3D motion capture of other species. DA - 2023 DB - OpenUCT DP - University of Cape Town KW - Engineering LK - https://open.uct.ac.za PY - 2023 T1 - WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion TI - WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion UR - http://hdl.handle.net/11427/39568 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/39568
dc.identifier.vancouvercitationJoska D. WildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion. []. ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/39568en_ZA
dc.language.rfc3066Eng
dc.publisher.departmentDepartment of Electrical Engineering
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.subjectEngineering
dc.titleWildPose: Long-Range 3D Motion Tracking of Cheetahs in the Wild Using Multi-Sensor Fusion
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
thesis_ebe_2023_joska daniel.pdf
Size:
12.82 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.72 KB
Format:
Item-specific license agreed upon to submission
Description:
Collections