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- W4313016888 abstract "Tracking the 3D motion of agile animals in the wild will enable new insight into the design of robotic controllers. However, in-field 3D pose estimation of high-speed wildlife such as cheetahs is still a challenge [1]. In this work, we aim to solve two of these challenges: unnatural pose estimates during highly occluded sequences and synchronization error between multi-view data. We expand on our previous Full Trajectory Estimation (FTE) method with two significant additions: Pairwise FTE (PW-FTE) and Shutter-delay FTE (SD-FTE). The PW-FTE expands on image-dependent pairwise terms, produced by a convolutional neural network (CNN), to infer occluded 2D keypoints, while SD-FTE uses shutter delay estimation to correct the synchronization error. Lastly, we combine both methods into PW-SD-FTE and perform a quantitative and qualitative analysis on a subset of AcinoSet, the video dataset of rapid and agile motions of cheetahs. We found that SD-FTE has significant benefits in tracking the position of the cheetah in the world frame, while PW-FTE provided a more robust 3D pose estimate during events of high occlusion. The PW-SD-FTE was found to retain both advantages, resulting in an improved baseline for AcinoSet. Code and data can be found at https://github.com/African-Robotics-Unit/AcinoSet/tree/pw_sd_fte." @default.
- W4313016888 created "2023-01-05" @default.
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- W4313016888 date "2022-10-23" @default.
- W4313016888 modified "2023-10-16" @default.
- W4313016888 title "Improving 3D Markerless Pose Estimation of Animals in the Wild using Low-Cost Cameras" @default.
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- W4313016888 doi "https://doi.org/10.1109/iros47612.2022.9981746" @default.
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