6D Pose Estimation on Spoons and Hands
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915273330655232 |
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| author | Tan, Kevin Yang, Fan Chen, Yuhao |
| author_facet | Tan, Kevin Yang, Fan Chen, Yuhao |
| contents | Accurate dietary monitoring is essential for promoting healthier eating habits. A key area of research is how people interact and consume food using utensils and hands. By tracking their position and orientation, it is possible to estimate the volume of food being consumed, or monitor eating behaviours, highly useful insights into nutritional intake that can be more reliable than popular methods such as self-reporting. Hence, this paper implements a system that analyzes stationary video feed of people eating, using 6D pose estimation to track hand and spoon movements to capture spatial position and orientation. In doing so, we examine the performance of two state-of-the-art (SOTA) video object segmentation (VOS) models, both quantitatively and qualitatively, and identify main sources of error within the system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02335 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 6D Pose Estimation on Spoons and Hands Tan, Kevin Yang, Fan Chen, Yuhao Computer Vision and Pattern Recognition Accurate dietary monitoring is essential for promoting healthier eating habits. A key area of research is how people interact and consume food using utensils and hands. By tracking their position and orientation, it is possible to estimate the volume of food being consumed, or monitor eating behaviours, highly useful insights into nutritional intake that can be more reliable than popular methods such as self-reporting. Hence, this paper implements a system that analyzes stationary video feed of people eating, using 6D pose estimation to track hand and spoon movements to capture spatial position and orientation. In doing so, we examine the performance of two state-of-the-art (SOTA) video object segmentation (VOS) models, both quantitatively and qualitatively, and identify main sources of error within the system. |
| title | 6D Pose Estimation on Spoons and Hands |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.02335 |