Food sharing and communal eating are very common in some countries. To assess individual dietary intake in food sharing scenarios, this work proposes a vision-based approach to first capturing the food sharing scenario with a 360-degree camera, and then using a neural network to infer different eating states of each individual based on their body pose and relative positions to the dishes. The number of bites each individual has taken of each dish is then deduced by analyzing the inferred eating states. A new dataset with 14 panoramic food sharing videos was constructed to validate our approach. The results show that our approach is able to reliably predict different eating states as well as individual’s bite count with respect to each dish in food sharing scenarios
