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A Low-cost autonomous tracking camera system for 3d marker-less motion capture of animals in the wild
The study of natural movement has long fascinated scientists, engineers and doctors. Today, motion capture research not only aids in medical diagnostics and rehabilitation but also enhances game and movie animations. Additionally, it contributes to the understanding of complex organic motions, infor...
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| Format: | Thesis |
| Language: | English English |
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Department of Electrical Engineering
2026
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| Summary: | The study of natural movement has long fascinated scientists, engineers and doctors. Today, motion capture research not only aids in medical diagnostics and rehabilitation but also enhances game and movie animations. Additionally, it contributes to the understanding of complex organic motions, informing the design of efficient, nature-mimicking robots. A large proportion of the research of human and animal motion capture relies on data captured using directional sensors with a limited field of view, such as RGB (red green blue) or RGB-D (red green blue-depth) cameras. Physical constraints limit the amount of data that can be collected with a single sensor (or set of sensors) since the subject is typically constrained to a specific capture area based on the sensor's field of view (FOV). This study focuses on the development of a camera-based system that can autonomously track a moving animal using rotating cameras to increase the amount of usable data that can be collected. In the pursuit of this objective, two systems were developed and tested. The first system consisted of a set of three cameras fixed to a rigid platform, with a camera on each end and the third midway between them. The platform was fixed to a brush-less DC (Direct Current) motor with the middle camera directly above the motor shaft. The second system consisted of an independent rotating camera fixed to the shaft of a brush-less DC motor. For both systems, the subject's position in the image frame of the camera mounted above the axis of rotation was determined using YOLO (You Only Look Once), a state-of-the-art object detection neural network. An extended Kalman filter (EKF) and full state feedback (FSF) controller were used to control the motor's position to keep the subject in the centre of the camera frame. DeepLabCut (DLC) was used to extract 2D key-points, and then a trajectory optimisation-based 3D pose estimation method called Full Trajectory Estimation (FTE) was used to reconstruct the 3D trajectories of the subject. Quantitative and qualitative experimental results are provided to validate the systems performance. Finally, this study concludes with recommendations for enhancing the system's performance, alongside proposed directions for future research and development in this field. |
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