Section: New Results
Recognition, Modelling and Description of Manipulation Actions
Participants : Nachwa Abou Bakr, James Crowley.
A full understanding of human actions requires: recognizing what action has been performed, predicting how it will affect the surrounding environment, explaining why this action has been performed, and who is performing it. Classic approaches to action recognition interpret a spatio-temporal pattern in a video sequence to tell what action has been performed, and perhaps how and where it was performed. A more complete understanding requires information about why the action was performed, and how it affects the environment. This face of understanding can be provided by explaining the action as part of a narrative.
We have addressed the problem of recognition, modelling and description of human activities, with results on three problems: (1) the use of transfer learning for simultaneous visual recognition of objects and object states, (2) the recognition of manipulation actions from state transitions, and (3) the interpretation of a series of actions and states as events in a predefined story to construct a narrative description.
These results have been developed using food preparation activities as an experimental domain. We start by recognizing food classes such as tomatoes and lettuce and food states, such as sliced and diced, during meal preparation. We adapt the VGG network architecture to jointly learn the representations of food items and food states using transfer learning. We model actions as the transformation of object states. We use recognised object properties (state and type) to detect corresponding manipulation actions by tracking object transformations in the video. Experimental performance evaluation for this approach is provided using the 50 salads and EPIC-Kitchen datasets. We use the resulting action descriptions to construct narrative descriptions for complex activities observed in videos of 50 salads dataset.