Egocentric Action Recognition by Capturing Hand-Object Contact and Object State

Improving the performance of egocentric action recognition (EAR) requires accurately capturing interactions between actors and objects. In this paper, we propose two learning methods that enable recognition models to capture hand-object contact and object state change. We introduce Hand-Object Contact Learning (HOCL), which enables the model to focus on hand-object contact during actions, and Object State Learning (OSL), which enables the model to focus on object state changes caused by hand actions. Evaluation using a CNN-based model and a transformer-based model on the EGTEA, MECCANO, and EPIC-KITCHENS 100 datasets demonstrated the effectiveness of applying HOCL and OSL. Their application improved overall accuracy by up to 2.24% on EGTEA, 3.97% on MECCANO, and 1.49% on EPIC-KITCHENS 100. In addition, HOCL and OSL improved the performance on data with small training samples and one from unfamiliar scenes. Qualitative analysis revealed that their application enabled the models to precisely capture the interaction between actor and object.