6D Pose Estimation on Spoons and Hands
This work addresses dietary monitoring for health promotion, but it is incremental as it applies existing methods to a specific domain without introducing new techniques.
The paper tackled the problem of dietary monitoring by implementing a system that uses 6D pose estimation to track hand and spoon movements from video to estimate food consumption and eating behaviors, examining the performance of two SOTA video object segmentation models and identifying error sources.
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.